{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基本操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组创建\n",
    "创建数组的最简单的方法就是使用array函数 ， 将Python下的list转换为ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "打印输出列表： [1, 2, 3, 4, 5]\n",
      "取出第一个数据： 1\n",
      "取出最后一个数据： 5\n",
      "取出最后一个数据： 5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "l = [1,2,3,4,5]\n",
    "print('打印输出列表：',l)\n",
    "\n",
    "print('取出第一个数据：',l[0])\n",
    "\n",
    "print('取出最后一个数据：', l[4])\n",
    "print('取出最后一个数据：', l[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "取出两个： [2, 3]\n"
     ]
    }
   ],
   "source": [
    "# 列表的切片操作，左闭右开\n",
    "print('取出两个：', l[1:3]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 np.array -- 强转为 ndarray类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建NumPy数组np.array(列表)\n",
    "arr = np.array(l)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[1,2]] # NumPy数组可以自由的取多个数据 -- 列表不可以这样操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True  True]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = arr >= 3 # 大于等于3的数字筛选！ -- 列表不可以这样操作\n",
    "print(cond)\n",
    "arr[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**不同创建方式：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建全是1的 数组\n",
    "np.ones(shape = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建全是0的 数组\n",
    "np.zeros(shape = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14,\n",
       "       3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建全是其他数字的 数组\n",
    "np.full(shape = 20,fill_value = 3.14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38, 27, 35, 97, 83, 24, 74, 74, 43, 21])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机数组，10个元素，各元素为0～99的整数\n",
    "np.random.randint(0,100,size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.98284534,  1.1425518 ,  0.22175356,  0.75224665, -0.37639924,\n",
       "       -1.0550602 , -0.6406787 ,  0.4225846 , -1.81878674,  0.18546992])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成10个元素的随机数组，各元素符合正态分布\n",
    "np.random.randn(10) # 正态分布，平均值是0，标准差是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.,  7.,  9., 11., 13., 15., 17., 19., 21., 23., 25.,\n",
       "       27., 29., 31., 33., 35., 37., 39., 41., 43., 45., 47., 49., 51.,\n",
       "       53., 55., 57., 59., 61., 63., 65., 67., 69., 71., 73., 75., 77.,\n",
       "       79., 81., 83., 85., 87., 89., 91., 93., 95., 97., 99.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1～99的等差数列，元素为50个\n",
    "np.linspace(1,99,50) # 等差数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1.,     3.,     9.,    27.,    81.,   243.,   729.,  2187.,\n",
       "        6561., 19683., 59049.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不显示科学技术法\n",
    "np.set_printoptions(suppress = True) \n",
    "\n",
    "# 等比数列：\n",
    "# np.logspace(start=开始值，stop=结束值，num=元素个数，base=指定对数的底, num=元素个数, endpoint=是否包含结束值)\n",
    "\n",
    "# 3**0开始，3**10结束\n",
    "np.logspace(0,10,base=3,num = 11) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8045119 , 0.83665337, 0.4495445 , 0.91689154, 0.6332068 ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0～1之间的随机小数组成的 数组\n",
    "np.random.random(size = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 等差数列\n",
    "np.arange(start = 0,stop = 20,step = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 6, 9, 1, 5],\n",
       "       [7, 0, 6, 3, 1],\n",
       "       [4, 7, 7, 6, 5]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 二维数组\n",
    "np.random.randint(0, 10, size=(3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[2, 5, 4, 1],\n",
       "        [0, 5, 8, 4],\n",
       "        [0, 1, 4, 6]],\n",
       "\n",
       "       [[3, 3, 5, 7],\n",
       "        [8, 3, 8, 4],\n",
       "        [8, 8, 0, 9]]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 三维数组\n",
    "np.random.randint(0, 10, size=(2, 3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.15198341, 0.02656466, 0.2752902 , 0.15327416, 0.37688311],\n",
       "       [0.66154792, 0.01372137, 0.53817618, 0.8961565 , 0.23962609],\n",
       "       [0.81533024, 0.45235796, 0.04904229, 0.28382124, 0.99289264]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机数组\n",
    "arr = np.random.random(size = (3,5))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看形状\n",
    "arr.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看元素类型\n",
    "arr.dtype "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看元素个数\n",
    "arr.size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数组维度\n",
    "arr.ndim "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个元素大小，对应8个字节\n",
    "arr.itemsize "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 文件IO操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**保存数组：**\n",
    "\n",
    "save方法保存ndarray到一个npy文件，也可以使用savez将多个array保存到一个.npz文件中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3, 6, 9]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(0,10,3)] # 列表生成式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NumPy的方法，功能类似\n",
    "arr = np.arange(0,10,3) \n",
    "\n",
    "# 保存数据\n",
    "np.save('./data/data1',arr) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**读取：**\n",
    "\n",
    "load方法来读取存储的数组，如果是.npz文件的话，读取之后相当于形成了一个key-value类型的变量，通过保存时定义的key来获取相应的array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 6, 9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('./data/data1.npy') # 取出数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.,\n",
       "        512., 1024.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 等比数列\n",
    "arr2 = np.logspace(0,10,base = 2,num = 11)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存多个数组 -- 字典形式保存\n",
    "np.savez('./data/data2.npz',arr1 = arr, arr2 = arr2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 6, 9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出arr1数组\n",
    "np.load('./data/data2.npz')['arr1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.,\n",
       "        512., 1024.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出arr2数组\n",
    "np.load('./data/data2.npz')['arr2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**读写csv、txt文件**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 5, 4, 5],\n",
       "       [0, 2, 4, 8],\n",
       "       [8, 4, 8, 6]], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成 3*4 的随机矩阵\n",
    "arr = np.random.randint(0, 10, size=(3, 4))\n",
    "\n",
    "# 储存数组到txt文件\n",
    "np.savetxt(\"arr.csv\", arr, delimiter=',')\n",
    "\n",
    "\n",
    "# 读取txt文件，delimiter为分隔符，dtype为数据类型\n",
    "np.loadtxt(\"arr.csv\", delimiter=',', dtype=np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ndarray的数据类型：\n",
    "- int: int8、uint8、int16、int32、int64\n",
    "- float: float16、float32、float64\n",
    "- str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "array创建时，指定:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 5], dtype=int16)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([2,4,5],dtype = np.int16)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 5], dtype=int32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([2,4,5],dtype = np.int32)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存\n",
    "arr = np.random.randint(0,100,size = (100,50),dtype = np.int8)\n",
    "np.save('./data/data3_int8',arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存\n",
    "arr = np.random.randint(0,100,size = (100,50),dtype = np.int64)\n",
    "np.save('./data/data4_int64',arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uint8 无符号，没有负号，没有负数\n",
    "\n",
    "# int8 \n",
    "2**8 # -128 ~ 127 包含0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-128, -127, -126, -125, -124, -123, -122, -121, -120, -119, -118,\n",
       "       -117, -116, -115, -114, -113, -112, -111, -110, -109, -108, -107,\n",
       "       -106, -105, -104, -103, -102, -101, -100,  -99,  -98,  -97,  -96,\n",
       "        -95,  -94,  -93,  -92,  -91,  -90,  -89,  -88,  -87,  -86,  -85,\n",
       "        -84,  -83,  -82,  -81,  -80,  -79,  -78,  -77,  -76,  -75,  -74,\n",
       "        -73,  -72,  -71,  -70,  -69,  -68,  -67,  -66,  -65,  -64,  -63,\n",
       "        -62,  -61,  -60,  -59,  -58,  -57,  -56,  -55,  -54,  -53,  -52,\n",
       "        -51,  -50,  -49,  -48,  -47,  -46,  -45,  -44,  -43,  -42,  -41,\n",
       "        -40,  -39,  -38,  -37,  -36,  -35,  -34,  -33,  -32,  -31,  -30,\n",
       "        -29,  -28,  -27,  -26,  -25,  -24,  -23,  -22,  -21,  -20,  -19,\n",
       "        -18,  -17,  -16,  -15,  -14,  -13,  -12,  -11,  -10,   -9,   -8,\n",
       "         -7,   -6,   -5,   -4,   -3,   -2,   -1,    0,    1,    2,    3,\n",
       "          4,    5,    6,    7,    8,    9,   10,   11,   12,   13,   14,\n",
       "         15,   16,   17,   18,   19,   20,   21,   22,   23,   24,   25,\n",
       "         26,   27,   28,   29,   30,   31,   32,   33,   34,   35,   36,\n",
       "         37,   38,   39,   40,   41,   42,   43,   44,   45,   46,   47,\n",
       "         48,   49,   50,   51,   52,   53,   54,   55,   56,   57,   58,\n",
       "         59,   60,   61,   62,   63,   64,   65,   66,   67,   68,   69,\n",
       "         70,   71,   72,   73,   74,   75,   76,   77,   78,   79,   80,\n",
       "         81,   82,   83,   84,   85,   86,   87,   88,   89,   90,   91,\n",
       "         92,   93,   94,   95,   96,   97,   98,   99,  100,  101,  102,\n",
       "        103,  104,  105,  106,  107,  108,  109,  110,  111,  112,  113,\n",
       "        114,  115,  116,  117,  118,  119,  120,  121,  122,  123,  124,\n",
       "        125,  126,  127], dtype=int8)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.arange(-128,128,dtype = np.int8)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
       "       141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,\n",
       "       167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
       "       180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,\n",
       "       193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,\n",
       "       206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218,\n",
       "       219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n",
       "       232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244,\n",
       "       245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,   0,   1,\n",
       "         2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
       "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,\n",
       "        41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,\n",
       "        54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,\n",
       "        67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,\n",
       "        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,\n",
       "        93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,\n",
       "       106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n",
       "       119, 120, 121, 122, 123, 124, 125, 126, 127], dtype=uint8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 ~ 255 刚好是256个数字，只能表示整数 0 ~ 255\n",
    "arr2 = np.arange(-128,128,dtype = np.uint8)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int8')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数组类型\n",
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据类型转换astype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-128., -127., -126., -125., -124., -123., -122., -121., -120.,\n",
       "       -119., -118., -117., -116., -115., -114., -113., -112., -111.,\n",
       "       -110., -109., -108., -107., -106., -105., -104., -103., -102.,\n",
       "       -101., -100.,  -99.,  -98.,  -97.,  -96.,  -95.,  -94.,  -93.,\n",
       "        -92.,  -91.,  -90.,  -89.,  -88.,  -87.,  -86.,  -85.,  -84.,\n",
       "        -83.,  -82.,  -81.,  -80.,  -79.,  -78.,  -77.,  -76.,  -75.,\n",
       "        -74.,  -73.,  -72.,  -71.,  -70.,  -69.,  -68.,  -67.,  -66.,\n",
       "        -65.,  -64.,  -63.,  -62.,  -61.,  -60.,  -59.,  -58.,  -57.,\n",
       "        -56.,  -55.,  -54.,  -53.,  -52.,  -51.,  -50.,  -49.,  -48.,\n",
       "        -47.,  -46.,  -45.,  -44.,  -43.,  -42.,  -41.,  -40.,  -39.,\n",
       "        -38.,  -37.,  -36.,  -35.,  -34.,  -33.,  -32.,  -31.,  -30.,\n",
       "        -29.,  -28.,  -27.,  -26.,  -25.,  -24.,  -23.,  -22.,  -21.,\n",
       "        -20.,  -19.,  -18.,  -17.,  -16.,  -15.,  -14.,  -13.,  -12.,\n",
       "        -11.,  -10.,   -9.,   -8.,   -7.,   -6.,   -5.,   -4.,   -3.,\n",
       "         -2.,   -1.,    0.,    1.,    2.,    3.,    4.,    5.,    6.,\n",
       "          7.,    8.,    9.,   10.,   11.,   12.,   13.,   14.,   15.,\n",
       "         16.,   17.,   18.,   19.,   20.,   21.,   22.,   23.,   24.,\n",
       "         25.,   26.,   27.,   28.,   29.,   30.,   31.,   32.,   33.,\n",
       "         34.,   35.,   36.,   37.,   38.,   39.,   40.,   41.,   42.,\n",
       "         43.,   44.,   45.,   46.,   47.,   48.,   49.,   50.,   51.,\n",
       "         52.,   53.,   54.,   55.,   56.,   57.,   58.,   59.,   60.,\n",
       "         61.,   62.,   63.,   64.,   65.,   66.,   67.,   68.,   69.,\n",
       "         70.,   71.,   72.,   73.,   74.,   75.,   76.,   77.,   78.,\n",
       "         79.,   80.,   81.,   82.,   83.,   84.,   85.,   86.,   87.,\n",
       "         88.,   89.,   90.,   91.,   92.,   93.,   94.,   95.,   96.,\n",
       "         97.,   98.,   99.,  100.,  101.,  102.,  103.,  104.,  105.,\n",
       "        106.,  107.,  108.,  109.,  110.,  111.,  112.,  113.,  114.,\n",
       "        115.,  116.,  117.,  118.,  119.,  120.,  121.,  122.,  123.,\n",
       "        124.,  125.,  126.,  127.], dtype=float32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转换其数据类型，为我们生成新的数组\n",
    "arr3 = arr1.astype(np.float32)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int8')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数组类型\n",
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数组运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**加减乘除幂运算：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 8, 9, 2, 0],\n",
       "       [9, 5, 1, 3, 7],\n",
       "       [3, 9, 8, 5, 0]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机数组\n",
    "nd = np.random.randint(0,10,size = (3,5))\n",
    "nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0, 512, 729,   8,   0],\n",
       "       [729, 125,   1,  27, 343],\n",
       "       [ 27, 729, 512, 125,   0]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 幂运算\n",
    "nd ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0, 512, 729,   8,   0],\n",
       "       [729, 125,   1,  27, 343],\n",
       "       [ 27, 729, 512, 125,   0]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 幂运算\n",
    "np.power(nd,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 4. , 4.5, 1. , 0. ],\n",
       "       [4.5, 2.5, 0.5, 1.5, 3.5],\n",
       "       [1.5, 4.5, 4. , 2.5, 0. ]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 除法运算\n",
    "nd / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, 0, 0],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取模运算\n",
    "nd % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 4, 4, 1, 0],\n",
       "       [4, 2, 0, 1, 3],\n",
       "       [1, 4, 4, 2, 0]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 整除运算\n",
    "nd // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1,  2, -1, -4])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加减运算\n",
    "arr1 = np.array([1, 2, 3, 4, 5])\n",
    "arr2 = np.array([2, 3, 1, 5, 9])\n",
    "arr1 - arr2  # 减法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  5,  4,  9, 14])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 + arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**逻辑运算：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 8, 9, 2, 0],\n",
       "       [9, 5, 1, 3, 7],\n",
       "       [3, 9, 8, 5, 0]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数组\n",
    "nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True,  True, False, False],\n",
       "       [ True, False, False, False,  True],\n",
       "       [False,  True,  True, False, False]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd > 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False],\n",
       "       [False, False, False, False, False],\n",
       "       [False, False, False, False, False]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd == 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 == arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 > arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数组与标量计算:**\n",
    "\n",
    "数组与标量的算术运算也会将标量值传播到各个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.5       , 0.33333333, 0.25      , 0.2       ,\n",
       "       0.16666667, 0.14285714, 0.125     , 0.11111111])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10, 15, 20, 25, 30, 35, 40, 45])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr*5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***=、+=、-=操作:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "nd += 100 # 在原来的基础上加上100:  nd = nd + 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 108, 109, 102, 100],\n",
       "       [109, 105, 101, 103, 107],\n",
       "       [103, 109, 108, 105, 100]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "UFuncTypeError",
     "evalue": "Cannot cast ufunc 'divide' output from dtype('float64') to dtype('int64') with casting rule 'same_kind'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [63]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nd \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
      "\u001b[0;31mUFuncTypeError\u001b[0m: Cannot cast ufunc 'divide' output from dtype('float64') to dtype('int64') with casting rule 'same_kind'"
     ]
    }
   ],
   "source": [
    "# error: 数组不支持 /=运算\n",
    "nd /= 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 8, 8, 8, 7],\n",
       "       [8, 8, 7, 8, 8],\n",
       "       [8, 8, 8, 8, 7]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 支持 //=运算\n",
    "nd //= 3\n",
    "nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 26, 26, 24, 23],\n",
       "       [26, 25, 23, 24, 25],\n",
       "       [24, 26, 26, 25, 23]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd -= 10\n",
    "nd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复制与视图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**完全没有复制:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "a = np.random.randint(0, 100, size=(4, 5))\n",
    "b = a  # a, b指向同一块内存\n",
    "print(a is b)  # 返回True，a和b是同一个对象的 不同名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1024,   29,   11,    6,   24],\n",
       "       [  84,   48,   92,    7,   29],\n",
       "       [  48,   54,   57,   35,   25],\n",
       "       [  40,   32,    9,    0,   31]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[1024,   29,   11,    6,   24],\n",
       "       [  84,   48,   92,    7,   29],\n",
       "       [  48,   54,   57,   35,   25],\n",
       "       [  40,   32,    9,    0,   31]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b[0,0] = 1024 \n",
    "\n",
    "# 命运共同体 \n",
    "display(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**查看或浅拷贝：**\n",
    "\n",
    "不同的数组对象可以共享相同的数据。该view方法创建一个查看相同数据的新数组对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 85, 35,  6, 37],\n",
       "       [28,  0, 84, 28, 68],\n",
       "       [50, 42, 49, 79, 37],\n",
       "       [61, 35, 36, 36, 82]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[34, 85, 35,  6, 37],\n",
       "       [28,  0, 84, 28, 68],\n",
       "       [50, 42, 49, 79, 37],\n",
       "       [61, 35, 36, 36, 82]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成随机数组\n",
    "a = np.random.randint(0, 100, size=(4, 5))\n",
    "\n",
    "b = a.view()  # 使用a中的数据，创建一个新数组对象\n",
    "display(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a is b  # 返回False，a和b是两个不同的对象，但指向同一个内存对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.base is a  # 返回True，b视图的根数据和a一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.flags.owndata  # 返回False，b中的数据不是其自己的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.flags.owndata  # 返回True，a中的数据是其自己的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1024,   85,   35,    6,   37],\n",
       "       [  28,    0,   84,   28,   68],\n",
       "       [  50,   42,   49,   79,   37],\n",
       "       [  61,   35,   36,   36,   82]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[1024,   85,   35,    6,   37],\n",
       "       [  28,    0,   84,   28,   68],\n",
       "       [  50,   42,   49,   79,   37],\n",
       "       [  61,   35,   36,   36,   82]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b[0, 0] = 1024  # a和b的数据都发生改变，他们指向同一块内存\n",
    "\n",
    "display(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**深拷贝：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机数组\n",
    "a = np.random.randint(0, 100, size=(4, 5))\n",
    "b = a.copy()\n",
    "b is a  # 返回False，a和b是不同的对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.base is a  # 返回False，b视图的根数据和a不一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.flags.owndata  # 返回True，b中的数据是其自己的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.flags.owndata  # 返回True,a中的数据是其自己的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28, 82, 83, 86, 98],\n",
       "       [55, 87, 67, 83, 94],\n",
       "       [88, 68, 22, 55, 44],\n",
       "       [ 3,  6, 22,  8, 92]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[1024,   82,   83,   86,   98],\n",
       "       [  55,   87,   67,   83,   94],\n",
       "       [  88,   68,   22,   55,   44],\n",
       "       [   3,    6,   22,    8,   92]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b[0, 0] = 1024  # b改变，a不变，分道扬镳\n",
    "\n",
    "display(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "copy应该在不再需要原来的数组情况下，切片后调用。\n",
    "\n",
    "例如，假设a是一个巨大的中间结果，而最终结果b仅包含的一小部分a，则在b使用切片进行构造时应制作一个深拷贝："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(1e8) \n",
    "b = a[::1000000].copy() # 每100万个数据中取一个数据，步长为1000000\n",
    "del a # 不在需要a，删除占大内存的a \n",
    "\n",
    "b.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 索引和切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一维数组索引和切片\n",
    "numpy中数组切片是原始数组的视图，这意味着数据不会被复制，视图上任何数据的修改都会反映到原数组上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 59, 96, 32, 43, 28, 75, 84,  3, 21])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机数组\n",
    "arr = np.random.randint(0,100,size = 10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 32, 28])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[0, 3, 5]]  # 索引，根据位置取相应数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32, 43, 28])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[3:6] # 切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32, 28])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[3:6:2] # 从3到6，2表示步长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 59, 96, 32, 43, 28, 75, 84,  3, 21])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:] # 默认全部数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 32, 75, 21])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[::3] # 3表示步长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 75, 32, 52])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[::-3] # 倒过来，取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二维数组索引和切片\n",
    "对于二维数组或者高维数组，我们可以按照之前的知识来索引，当然也可以传入一个以逗号隔开的索引列表来选区单个或多个元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33, 46, 17, 75, 27],\n",
       "       [81, 65, 29, 60, 60],\n",
       "       [17, 55, 34, 88, 20]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机生成数组\n",
    "arr2 = np.random.randint(0, 100, size=(3, 5))\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 逗号隔开，等于arr2[0][2]\n",
    "arr2[0,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([65, 29])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2[1,[1,2]] # 第二行，第二列和第三列数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[60, 60],\n",
       "       [88, 20]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第二行和第三行的 第四列之后的数据\n",
    "# 索引和切片配合使用\n",
    "arr2[[1,2],3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[60, 60],\n",
       "       [88, 20]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第二行和第三行的，第四列和第五列数据\n",
    "arr2[[1,2]][:,[3,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33, 17, 27],\n",
       "       [17, 34, 20]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第一行和第三行的，第1，3，5列\n",
    "arr2[[0,2]][:,[0,2,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  33,   46,   17,   75,   27],\n",
       "       [  81,   65,   29,   60,   60],\n",
       "       [  17,   55, 1024,   88, 1024]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第3行，第3列和第5列 变为1024\n",
    "arr2[2,[2,4]] = 1024\n",
    "arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 花式索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整数数组进行索引即花式索引,其和切片不一样，它总是将数据复制到新数组中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([74, 71, 75,  1, 10, 49, 36, 44, 65, 28, 97, 43, 44, 70, 56, 23, 21,\n",
       "       89, 21, 86])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数组长度为20\n",
    "arr = np.random.randint(0,100,size = 20)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 49, 44])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[3,5,7]] # 花式索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([74, 71, 75, 65, 97, 70, 89, 86])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond = arr > 60\n",
    "arr[cond] # boolean花式索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 10, 28, 97, 23, 21, 89, 21, 86])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出小于30，大于80数据\n",
    "cond1 = arr < 30\n",
    "cond2 = arr > 80\n",
    "\n",
    "# Numpy 中 | 相当于 or\n",
    "cond = cond1 | cond2 # 或运算\n",
    "arr[cond]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一维："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成随机数组\n",
    "arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 4, 6, 8, 8, 8])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 花式索引\n",
    "arr2 = arr1[[1, 3, 3, 5, 7, 7, 7]]  \n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2[-1] = 1024  # 修改值，不影响arr1\n",
    "arr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二维："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array([\n",
    "    [1, 3, 5, 7, 9],\n",
    "    [2, 4, 6, 8, 10],\n",
    "    [12, 18, 22, 23, 37],\n",
    "    [123, 55, 17, 88, 103]])  # shape(4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   4,   6,   8,  10],\n",
       "       [123,  55,  17,  88, 103]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取第二行和第四行\n",
    "arr2d[[1, 3]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6, 103])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相当于 arr2d[1,2]获取一个元素, arr2d[3,4]获取另一个元素\n",
    "arr2d[([1, 3], [2, 4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6,  10,  10],\n",
       "       [ 17, 103, 103],\n",
       "       [ 17, 103, 103],\n",
       "       [ 17, 103, 103]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选择一个区域，相当于 arr2d[[1, 3, 3, 3]][:, [2, 4, 4]]\n",
    "arr2d[np.ix_([1,3,3,3],[2,4,4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ix_()函数可用于组合不同的向量\n",
    "# 第一个列表存的是待提取元素的行标，第二个列表存的是待提取元素的列标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "boolean值 索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False,  True, False, False, False, False,\n",
       "       False])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = np.array(['softpo', 'Brandon', 'Will', 'Michael',\n",
    "                 'Will', 'Ella', 'Daniel', 'softpo', 'W ill', 'Brandon'])\n",
    "\n",
    "cond1 = names == 'Will'\n",
    "cond1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Will', 'Will'], dtype='<U7')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[cond1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False, False,  True],\n",
       "       [False, False, False, False,  True, False, False, False],\n",
       "       [False,  True, False, False, False, False, False, False],\n",
       "       [False, False,  True, False, False, False, False,  True],\n",
       "       [False, False, False, False, False, False,  True, False],\n",
       "       [False, False, False, False, False, False, False, False],\n",
       "       [False, False, False, False,  True, False, False, False],\n",
       "       [False, False, False, False,  True, False, False, False],\n",
       "       [False, False, False, False, False, False, False, False]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randint(0, 100, size=(10, 8))  # 0~100随机数\n",
    "cond2 = arr > 90  # 找到所有大于90的索引，返回boolean类型的数组\n",
    "cond2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([91, 94, 91, 98, 94, 92, 98, 96])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[cond2]\n",
    "# 返回数据全部是大于90的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 形状操作\n",
    "## 数组变形\n",
    "reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50, 95, 19, 58],\n",
       "       [70, 69, 82, 93],\n",
       "       [78, 27, 99, 43]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成随机矩阵\n",
    "nd2 = np.random.randint(0, 100, size=(3, 4))\n",
    "\n",
    "display(nd2)  # 美观"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4行3列：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[50, 95, 19],\n",
       "       [58, 70, 69],\n",
       "       [82, 93, 78],\n",
       "       [27, 99, 43]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2行6列：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[50, 95, 19, 58, 70, 69],\n",
       "       [82, 93, 78, 27, 99, 43]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据重塑 reshape\n",
    "print('4行3列：')\n",
    "display(nd2.reshape(4, 3)) \n",
    "\n",
    "print('2行6列：')\n",
    "display(nd2.reshape(2, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50, 95, 19, 58, 70, 69],\n",
       "       [82, 93, 78, 27, 99, 43]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -1表示最后算，相当于 x * 6 = 3 * 4----> x = 2\n",
    "display(nd2.reshape(-1, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 95, 19, 58, 70, 69, 82, 93, 78, 27, 99, 43])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(nd2.reshape(-1))  # 自动算，变成一维"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 8, 5, 7],\n",
       "       [1, 2, 2, 7, 3],\n",
       "       [6, 2, 7, 2, 9]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.random.randint(0, 10, size=(3, 5))\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 1, 6],\n",
       "       [4, 2, 2],\n",
       "       [8, 2, 7],\n",
       "       [5, 7, 2],\n",
       "       [7, 3, 9]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.T  # 转置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[6, 4, 9, 8],\n",
       "        [4, 8, 6, 4],\n",
       "        [9, 2, 4, 8],\n",
       "        [2, 5, 1, 0],\n",
       "        [8, 2, 8, 2],\n",
       "        [2, 7, 7, 8]],\n",
       "\n",
       "       [[9, 6, 5, 9],\n",
       "        [3, 0, 5, 7],\n",
       "        [5, 4, 2, 7],\n",
       "        [0, 9, 3, 5],\n",
       "        [6, 2, 6, 5],\n",
       "        [8, 1, 7, 3]],\n",
       "\n",
       "       [[7, 2, 0, 9],\n",
       "        [9, 2, 2, 2],\n",
       "        [5, 1, 4, 7],\n",
       "        [2, 4, 7, 4],\n",
       "        [1, 0, 8, 4],\n",
       "        [2, 2, 3, 8]]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 三维数组\n",
    "arr2 = np.random.randint(0, 10, size=(3, 6, 4)) # shape(3, 6, 4)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[6, 4, 9, 2, 8, 2],\n",
       "        [9, 3, 5, 0, 6, 8],\n",
       "        [7, 9, 5, 2, 1, 2]],\n",
       "\n",
       "       [[4, 8, 2, 5, 2, 7],\n",
       "        [6, 0, 4, 9, 2, 1],\n",
       "        [2, 2, 1, 4, 0, 2]],\n",
       "\n",
       "       [[9, 6, 4, 1, 8, 7],\n",
       "        [5, 5, 2, 3, 6, 7],\n",
       "        [0, 2, 4, 7, 8, 3]],\n",
       "\n",
       "       [[8, 4, 8, 0, 2, 8],\n",
       "        [9, 7, 7, 5, 5, 3],\n",
       "        [9, 2, 7, 4, 4, 8]]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(arr2, axes=(2, 0, 1))  # transpose改变数组维度 shape(4,3,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组堆叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 7, 1, 2],\n",
       "       [0, 6, 1, 3]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0, -5,  4, -3],\n",
       "       [ 1, -3,  1, -1],\n",
       "       [-5,  1, -3, -1]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = np.random.randint(0, 10, size=(2, 4))\n",
    "arr2 = np.random.randint(-5, 5, size=(3, 4))\n",
    "\n",
    "display(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  7,  1,  2],\n",
       "       [ 0,  6,  1,  3],\n",
       "       [ 0, -5,  4, -3],\n",
       "       [ 1, -3,  1, -1],\n",
       "       [-5,  1, -3, -1],\n",
       "       [ 8,  7,  1,  2],\n",
       "       [ 0,  6,  1,  3]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并，默认进行 行合并，行变多\n",
    "np.concatenate([arr1, arr2, arr1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 0, 8, 2, 5],\n",
       "       [9, 7, 1, 3, 7],\n",
       "       [6, 9, 1, 4, 0]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[-2, -2, -2, -3],\n",
       "       [ 4,  0,  2,  0],\n",
       "       [-4,  3, -5,  4]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = np.random.randint(0, 10, size=(3, 5))\n",
    "\n",
    "arr2 = np.random.randint(-5, 5, size=(3, 4))\n",
    "\n",
    "display(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  0,  8,  2,  5, -2, -2, -2, -3],\n",
       "       [ 9,  7,  1,  3,  7,  4,  0,  2,  0],\n",
       "       [ 6,  9,  1,  4,  0, -4,  3, -5,  4]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis = 0默认值，代表按行合并\n",
    "# axis = 1表示按列合并，-1表示倒着数\n",
    "np.concatenate([arr1, arr2], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  0,  8,  2,  5, -2, -2, -2, -3],\n",
       "       [ 9,  7,  1,  3,  7,  4,  0,  2,  0],\n",
       "       [ 6,  9,  1,  4,  0, -4,  3, -5,  4]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr1, arr2], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组拆分\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[90, 86, 40, 94, 13, 36, 81, 10,  8],\n",
       "       [ 1, 81, 25, 52, 63, 85, 25, 69, 90],\n",
       "       [31, 14, 48, 95, 90,  9, 89, 24, 95],\n",
       "       [76, 42, 73, 86,  6, 33, 61, 87,  8],\n",
       "       [ 0, 49, 37, 90, 84, 17, 77, 69, 17],\n",
       "       [48, 44, 88, 86, 38, 76, 22, 30, 45]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[array([[90, 86, 40, 94, 13, 36, 81, 10,  8],\n",
       "        [ 1, 81, 25, 52, 63, 85, 25, 69, 90],\n",
       "        [31, 14, 48, 95, 90,  9, 89, 24, 95]]),\n",
       " array([[76, 42, 73, 86,  6, 33, 61, 87,  8],\n",
       "        [ 0, 49, 37, 90, 84, 17, 77, 69, 17],\n",
       "        [48, 44, 88, 86, 38, 76, 22, 30, 45]])]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机矩阵\n",
    "nd = np.random.randint(0, 100, size=(6, 9))\n",
    "\n",
    "display(nd)\n",
    "\n",
    "# 行拆分，一个数字，表示 分成多少份\n",
    "np.split(nd, indices_or_sections=2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "行拆分：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[90, 86, 40, 94, 13, 36, 81, 10,  8]]),\n",
       " array([[ 1, 81, 25, 52, 63, 85, 25, 69, 90],\n",
       "        [31, 14, 48, 95, 90,  9, 89, 24, 95],\n",
       "        [76, 42, 73, 86,  6, 33, 61, 87,  8]]),\n",
       " array([[ 0, 49, 37, 90, 84, 17, 77, 69, 17]]),\n",
       " array([[48, 44, 88, 86, 38, 76, 22, 30, 45]])]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表，表示 以第2,5,6行 为分割线，进行拆分\n",
    "print('行拆分：')\n",
    "np.split(nd, [1, 4, 5], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "列拆分：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[90],\n",
       "        [ 1],\n",
       "        [31],\n",
       "        [76],\n",
       "        [ 0],\n",
       "        [48]]),\n",
       " array([[86, 40, 94],\n",
       "        [81, 25, 52],\n",
       "        [14, 48, 95],\n",
       "        [42, 73, 86],\n",
       "        [49, 37, 90],\n",
       "        [44, 88, 86]]),\n",
       " array([[13],\n",
       "        [63],\n",
       "        [90],\n",
       "        [ 6],\n",
       "        [84],\n",
       "        [38]]),\n",
       " array([[36, 81, 10,  8],\n",
       "        [85, 25, 69, 90],\n",
       "        [ 9, 89, 24, 95],\n",
       "        [33, 61, 87,  8],\n",
       "        [17, 77, 69, 17],\n",
       "        [76, 22, 30, 45]])]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表，表示 以第2,5,6列 为分割线，进行拆分\n",
    "print('列拆分：')\n",
    "np.split(nd, [1, 4, 5], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 广播机制\n",
    "当两个数组的形状并不相同的时候，我们可以通过扩展数组的方法来实现相加、相减、相乘等操作。\n",
    "\n",
    "这种机制叫做**广播（broadcasting）**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一维数组广播\n",
    "\n",
    "![](1_NumPy科学计算库_images/01.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 3, 4],\n",
       "       [9, 6, 9],\n",
       "       [6, 0, 6],\n",
       "       [1, 9, 8],\n",
       "       [2, 1, 3]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 8,  5,  7],\n",
       "       [10,  8, 12],\n",
       "       [ 7,  2,  9],\n",
       "       [ 2, 11, 11],\n",
       "       [ 3,  3,  6]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.random.randint(0, 10, size=(5, 3))\n",
    "arr2 = np.arange(1, 4)\n",
    "\n",
    "display(arr1, arr2)\n",
    "\n",
    "# arr2 只有一行，arr1 有5行\n",
    "# 广播机制，arr2 变成了5份，每一份和每一行相加\n",
    "arr1 + arr2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二维数组的广播\n",
    "\n",
    "![](1_NumPy科学计算库_images/02.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2, 9, 0, 8],\n",
       "       [1, 1, 9, 9, 2],\n",
       "       [9, 5, 6, 3, 1],\n",
       "       [7, 8, 5, 9, 5]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([3.8, 4.4, 4.8, 6.8])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[3.8],\n",
       "       [4.4],\n",
       "       [4.8],\n",
       "       [6.8]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4 * 5 矩阵\n",
    "arr3 = np.random.randint(0, 10, size=(4, 5))\n",
    "\n",
    "# 每一行的平均值\n",
    "arr4 = arr3.mean(axis=1)\n",
    "\n",
    "display(arr3, arr4)\n",
    "\n",
    "# 形状改变，4行一列\n",
    "display(arr4.reshape(4, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.8, -1.8,  5.2, -3.8,  4.2],\n",
       "       [-3.4, -3.4,  4.6,  4.6, -2.4],\n",
       "       [ 4.2,  0.2,  1.2, -1.8, -3.8],\n",
       "       [ 0.2,  1.2, -1.8,  2.2, -1.8]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arr3有4列，arr4改变形状，变成了4列\n",
    "# 广播\n",
    "arr3 - arr4.reshape(4, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三维数组的广播\n",
    "\n",
    "![](1_NumPy科学计算库_images/03.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "三维数组：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[0, 1],\n",
       "        [2, 3],\n",
       "        [4, 5],\n",
       "        [6, 7]],\n",
       "\n",
       "       [[0, 1],\n",
       "        [2, 3],\n",
       "        [4, 5],\n",
       "        [6, 7]],\n",
       "\n",
       "       [[0, 1],\n",
       "        [2, 3],\n",
       "        [4, 5],\n",
       "        [6, 7]]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "二维数组：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  2],\n",
       "        [ 4,  6],\n",
       "        [ 8, 10],\n",
       "        [12, 14]],\n",
       "\n",
       "       [[ 0,  2],\n",
       "        [ 4,  6],\n",
       "        [ 8, 10],\n",
       "        [12, 14]],\n",
       "\n",
       "       [[ 0,  2],\n",
       "        [ 4,  6],\n",
       "        [ 8, 10],\n",
       "        [12, 14]]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 三维数组\n",
    "arr1 = np.array([0, 1, 2, 3, 4, 5, 6, 7]*3).reshape(3, 4, 2)  # shape(3,4,2)\n",
    "\n",
    "# 二维数组\n",
    "arr2 = np.array([0, 1, 2, 3, 4, 5, 6, 7]).reshape(4, 2)  # shape(4,2)\n",
    "\n",
    "print('三维数组：')\n",
    "display(arr1)\n",
    "\n",
    "print('二维数组：')\n",
    "display(arr2)\n",
    "\n",
    "arr3 = arr1 + arr2  # arr2数组在0维上复制3份 shape(3,4,2)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通用函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 元素级数字函数\n",
    "abs、sqrt、square、exp、log、sin、cos、tan，maxinmum、minimum、all、any、inner、clip、round、trace、ceil、floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(np.pi/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(np.pi/2).round(1) # 保留1位小数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(1024) # 开平方"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平方\n",
    "np.square(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 立方\n",
    "def fun(x):\n",
    "    return x**3\n",
    "\n",
    "fun(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 开立方\n",
    "def fun2(x):\n",
    "    return x**(1/3)\n",
    "\n",
    "fun2(27) # 27 = 3 * 3 * 3  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "729"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 幂运算\n",
    "np.power(9,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9999999999999996"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 幂运算\n",
    "np.power(64, 1/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# log2\n",
    "np.log2(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 3, 9, 3, 9, 8])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取最大值\n",
    "x = np.array([1, 5, 2, 9, 3, 6, 8])\n",
    "y = np.array([2, 4, 3, 7, 1, 9, 0])\n",
    "np.maximum(x, y)  # 返回两个数组中的 对应位置中，比较大的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 2],\n",
       "       [2, 6]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([9, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([85, 30])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 内积\n",
    "\n",
    "# 随机生成数组\n",
    "arr2 = np.random.randint(0, 10, size=(2, 2))\n",
    "display(arr2) \n",
    "display(arr2[0]) # 取出第一行\n",
    "\n",
    "np.inner(arr2[0], arr2)  # 返回一维数组向量内积，85 = 9*9 + 2*2，30 = 2*9 + 6*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向上取整\n",
    "a = 3.9999\n",
    "np.ceil(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向下取整\n",
    "np.floor(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 20, 22, 16, 19, 10,  1, 18, 24, 17, 19,  2, 12,  9, 25,  1,  8,\n",
       "       11, 26,  9])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机生成矩阵\n",
    "arr = np.random.randint(0,30,size = (20))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 20, 20, 16, 19, 10, 10, 18, 20, 17, 19, 10, 12, 10, 20, 10, 10,\n",
       "       11, 20, 10])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clip裁剪\n",
    "# 10：小于10，拔高，变成10\n",
    "# 20：大于20，削减，变成20\n",
    "np.clip(arr,10,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## where函数\n",
    "where 函数，三个参数，条件为真时选择值的数组，条件为假时选择值的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  5,  7, 10])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array([1, 3, 5, 7, 9])\n",
    "arr2 = np.array([2, 4, 6, 8, 10])\n",
    "\n",
    "cond = np.array([True, False, True, True, False])\n",
    "\n",
    "# 根据条件进行筛选\n",
    "np.where(cond, arr1, arr2)  # True选择arr1，False选择arr2的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29, 20, 15, 17, 24,  6, 10, 26, 23, 21, 27, 27, 11,  8, 16, 14, 26,\n",
       "       25, 12, 28])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([1024, 1024, 1024, 1024, 1024,    6,   10, 1024, 1024, 1024, 1024,\n",
       "       1024,   11,    8, 1024,   14, 1024, 1024,   12, 1024])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.random.randint(0,30,size = 20)\n",
    "\n",
    "display(arr3)\n",
    "\n",
    "np.where(arr3 < 15,arr3,1024) # 小于15还是自身的值，大于15设置成1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([118,  26, 121,  83,  89, 105, 174,   1,  -9, 121, 124,  23,  54,\n",
       "        89, 177, 167, 179, 131,  39,  52, 150,  61,  87, 129,  54, 142,\n",
       "       117,  16,  95, 116,  76, 144,  28,  78, 146,  99, 128,  62, 102,\n",
       "       151, 169,  74, 151, 166,  26, 169, 102, 155,  84,  75])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高考数学，成绩 0～150分，异常的成绩统一调整为 -1024\n",
    "\n",
    "# 随机生成成绩\n",
    "arr4 = np.random.randint(-10,180,size = 50)\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  118,    26,   121,    83,    89,   105, -1024,     1, -1024,\n",
       "         121,   124,    23,    54,    89, -1024, -1024, -1024,   131,\n",
       "          39,    52,   150,    61,    87,   129,    54,   142,   117,\n",
       "          16,    95,   116,    76,   144,    28,    78,   146,    99,\n",
       "         128,    62,   102, -1024, -1024,    74, -1024, -1024,    26,\n",
       "       -1024,   102, -1024,    84,    75])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where((arr4 < 0) | (arr4 > 150),-1024, arr4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  4,  5,  6,  9, 11, 15, 17])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([9, 3, 11, 6, 17, 5, 4, 15, 1])\n",
    "\n",
    "arr.sort()  # 直接改变原数组\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  4,  5,  6,  9, 11, 15, 17])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 返回深拷贝排序结果 \n",
    "np.sort(arr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 1, 6, 5, 3, 0, 2, 7, 4])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([9, 3, 11, 6, 17, 5, 4, 15, 1])\n",
    "arr.argsort()  # 返回从小到大排序索引 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 集合运算函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "交集：\n",
      "[4 6]\n",
      "并集：\n",
      " [2 3 4 5 6 8]\n",
      "差集：\n",
      " [2 8]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([2, 4, 6, 8])\n",
    "B = np.array([3, 4, 5, 6])\n",
    "\n",
    "print('交集：')\n",
    "print(np.intersect1d(A, B))  # 交集 \n",
    "\n",
    "# 两者合并，去重\n",
    "print('并集：\\n', np.union1d(A, B))  # 并集 \n",
    "\n",
    "# 差集：A中有，B中没有\n",
    "print('差集：\\n', np.setdiff1d(A, B))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数学和统计函数\n",
    "min、max、mean、median、sum、std、var、cumsum、cumprod、argmin、argmax、\n",
    "argwhere、cov、corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([1, 7, 2, 19, 23, 0, 88, 11, 6, 11])\n",
    "arr1.min()  # 返回最小值 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.argmax()  # 返回最大值的索引 返回 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4],\n",
       "       [6]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argwhere(arr1 > 20)  # 返回大于20的元素的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   8,  10,  29,  52,  52, 140, 151, 157, 168])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(arr1)  # 计算累加和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5, 3, 1, 4],\n",
       "       [4, 0, 4, 4, 2],\n",
       "       [2, 1, 3, 2, 7],\n",
       "       [4, 6, 3, 1, 2]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机矩阵\n",
    "arr2 = np.random.randint(0, 10, size=(4, 5))\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.75, 3.  , 3.25, 2.  , 3.75])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.mean(axis=0)  # 计算列的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.6, 2.8, 3. , 3.2])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.mean(axis=1)  # 计算行的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.8 , -1.6 ,  0.  ,  2.6 ],\n",
       "       [-1.6 ,  3.2 ,  0.  , -2.2 ],\n",
       "       [ 0.  ,  0.  ,  5.5 , -2.25],\n",
       "       [ 2.6 , -2.2 , -2.25,  3.7 ]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(arr2, rowvar=True)  # 协方差矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.53452248,  0.        ,  0.8077807 ],\n",
       "       [-0.53452248,  1.        ,  0.        , -0.63936201],\n",
       "       [ 0.        ,  0.        ,  1.        , -0.49876999],\n",
       "       [ 0.8077807 , -0.63936201, -0.49876999,  1.        ]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(arr2, rowvar=True)  # 相关性系数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 矩阵运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "点乘，矩阵运算:\n",
      " [[ 25  23]\n",
      " [ -4 -11]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 25,  23],\n",
       "       [ -4, -11]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#矩阵的乘积\n",
    "A = np.array([[4,2,3],\n",
    "              [1,3,1]]) # shape(2,3)\n",
    "B = np.array([[2,7],\n",
    "              [-5,-7],\n",
    "              [9,3]]) # shape(3,2)\n",
    "\n",
    "print('点乘，矩阵运算:\\n', np.dot(A,B))\n",
    "\n",
    "\n",
    "A @ B # 符号@ 表示矩阵乘积运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 25,  23],\n",
       "       [ -4, -11]])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2. ,  0.5,  0.5],\n",
       "       [ 0. ,  2. , -1. ],\n",
       "       [ 1. , -1.5,  0.5]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算矩阵的逆\n",
    "\n",
    "# 不使用科学计数法\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "from numpy.linalg import inv,det,eig,qr,svd\n",
    "A = np.array([[1,2,3],\n",
    "              [2,3,4],\n",
    "              [4,5,8]]) # shape(3,3)\n",
    "B = inv(A) # 逆矩阵\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9999999999999998"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "det(A) # 计算矩阵行列式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实战"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "简单排序前： [[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.2]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.6 1.4 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "\n",
    "data = np.loadtxt('./data/iris.csv',delimiter = ',') # 读取数据文件，data是二维的数组 \n",
    "print('简单排序前：', data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "简单排序后： [[0.2 1.4 3.5 5.1]\n",
      " [0.2 1.4 3.  4.9]\n",
      " [0.2 1.3 3.2 4.7]\n",
      " [0.2 1.5 3.1 4.6]\n",
      " [0.2 1.4 3.6 5. ]\n",
      " [0.4 1.7 3.9 5.4]\n",
      " [0.3 1.4 3.4 4.6]\n",
      " [0.2 1.5 3.4 5. ]\n",
      " [0.2 1.4 2.9 4.4]\n",
      " [0.1 1.5 3.1 4.9]\n",
      " [0.2 1.5 3.7 5.4]\n",
      " [0.2 1.6 3.4 4.8]\n",
      " [0.1 1.4 3.  4.8]\n",
      " [0.1 1.1 3.  4.3]\n",
      " [0.2 1.2 4.  5.8]\n",
      " [0.4 1.5 4.4 5.7]\n",
      " [0.4 1.3 3.9 5.4]\n",
      " [0.3 1.4 3.5 5.1]\n",
      " [0.3 1.7 3.8 5.7]\n",
      " [0.3 1.5 3.8 5.1]\n",
      " [0.2 1.7 3.4 5.4]\n",
      " [0.4 1.5 3.7 5.1]\n",
      " [0.2 1.  3.6 4.6]\n",
      " [0.5 1.7 3.3 5.1]\n",
      " [0.2 1.9 3.4 4.8]\n",
      " [0.2 1.6 3.  5. ]\n",
      " [0.4 1.6 3.4 5. ]\n",
      " [0.2 1.5 3.5 5.2]\n",
      " [0.2 1.4 3.4 5.2]\n",
      " [0.2 1.6 3.2 4.7]\n",
      " [0.2 1.6 3.1 4.8]\n",
      " [0.4 1.5 3.4 5.4]\n",
      " [0.1 1.5 4.1 5.2]\n",
      " [0.2 1.4 4.2 5.5]\n",
      " [0.2 1.5 3.1 4.9]\n",
      " [0.2 1.2 3.2 5. ]\n",
      " [0.2 1.3 3.5 5.5]\n",
      " [0.1 1.4 3.6 4.9]\n",
      " [0.2 1.3 3.  4.4]\n",
      " [0.2 1.5 3.4 5.1]\n",
      " [0.3 1.3 3.5 5. ]\n",
      " [0.3 1.3 2.3 4.5]\n",
      " [0.2 1.3 3.2 4.4]\n",
      " [0.6 1.6 3.5 5. ]\n",
      " [0.4 1.9 3.8 5.1]\n",
      " [0.3 1.4 3.  4.8]\n",
      " [0.2 1.6 3.8 5.1]\n",
      " [0.2 1.4 3.2 4.6]\n",
      " [0.2 1.5 3.7 5.3]\n",
      " [0.2 1.4 3.3 5. ]\n",
      " [1.4 3.2 4.7 7. ]\n",
      " [1.5 3.2 4.5 6.4]\n",
      " [1.5 3.1 4.9 6.9]\n",
      " [1.3 2.3 4.  5.5]\n",
      " [1.5 2.8 4.6 6.5]\n",
      " [1.3 2.8 4.5 5.7]\n",
      " [1.6 3.3 4.7 6.3]\n",
      " [1.  2.4 3.3 4.9]\n",
      " [1.3 2.9 4.6 6.6]\n",
      " [1.4 2.7 3.9 5.2]\n",
      " [1.  2.  3.5 5. ]\n",
      " [1.5 3.  4.2 5.9]\n",
      " [1.  2.2 4.  6. ]\n",
      " [1.4 2.9 4.7 6.1]\n",
      " [1.3 2.9 3.6 5.6]\n",
      " [1.4 3.1 4.4 6.7]\n",
      " [1.5 3.  4.5 5.6]\n",
      " [1.  2.7 4.1 5.8]\n",
      " [1.5 2.2 4.5 6.2]\n",
      " [1.1 2.5 3.9 5.6]\n",
      " [1.8 3.2 4.8 5.9]\n",
      " [1.3 2.8 4.  6.1]\n",
      " [1.5 2.5 4.9 6.3]\n",
      " [1.2 2.8 4.7 6.1]\n",
      " [1.3 2.9 4.3 6.4]\n",
      " [1.4 3.  4.4 6.6]\n",
      " [1.4 2.8 4.8 6.8]\n",
      " [1.7 3.  5.  6.7]\n",
      " [1.5 2.9 4.5 6. ]\n",
      " [1.  2.6 3.5 5.7]\n",
      " [1.1 2.4 3.8 5.5]\n",
      " [1.  2.4 3.7 5.5]\n",
      " [1.2 2.7 3.9 5.8]\n",
      " [1.6 2.7 5.1 6. ]\n",
      " [1.5 3.  4.5 5.4]\n",
      " [1.6 3.4 4.5 6. ]\n",
      " [1.5 3.1 4.7 6.7]\n",
      " [1.3 2.3 4.4 6.3]\n",
      " [1.3 3.  4.1 5.6]\n",
      " [1.3 2.5 4.  5.5]\n",
      " [1.2 2.6 4.4 5.5]\n",
      " [1.4 3.  4.6 6.1]\n",
      " [1.2 2.6 4.  5.8]\n",
      " [1.  2.3 3.3 5. ]\n",
      " [1.3 2.7 4.2 5.6]\n",
      " [1.2 3.  4.2 5.7]\n",
      " [1.3 2.9 4.2 5.7]\n",
      " [1.3 2.9 4.3 6.2]\n",
      " [1.1 2.5 3.  5.1]\n",
      " [1.3 2.8 4.1 5.7]\n",
      " [2.5 3.3 6.  6.3]\n",
      " [1.9 2.7 5.1 5.8]\n",
      " [2.1 3.  5.9 7.1]\n",
      " [1.8 2.9 5.6 6.3]\n",
      " [2.2 3.  5.8 6.5]\n",
      " [2.1 3.  6.6 7.6]\n",
      " [1.7 2.5 4.5 4.9]\n",
      " [1.8 2.9 6.3 7.3]\n",
      " [1.8 2.5 5.8 6.7]\n",
      " [2.5 3.6 6.1 7.2]\n",
      " [2.  3.2 5.1 6.5]\n",
      " [1.9 2.7 5.3 6.4]\n",
      " [2.1 3.  5.5 6.8]\n",
      " [2.  2.5 5.  5.7]\n",
      " [2.4 2.8 5.1 5.8]\n",
      " [2.3 3.2 5.3 6.4]\n",
      " [1.8 3.  5.5 6.5]\n",
      " [2.2 3.8 6.7 7.7]\n",
      " [2.3 2.6 6.9 7.7]\n",
      " [1.5 2.2 5.  6. ]\n",
      " [2.3 3.2 5.7 6.9]\n",
      " [2.  2.8 4.9 5.6]\n",
      " [2.  2.8 6.7 7.7]\n",
      " [1.8 2.7 4.9 6.3]\n",
      " [2.1 3.3 5.7 6.7]\n",
      " [1.8 3.2 6.  7.2]\n",
      " [1.8 2.8 4.8 6.2]\n",
      " [1.8 3.  4.9 6.1]\n",
      " [2.1 2.8 5.6 6.4]\n",
      " [1.6 3.  5.8 7.2]\n",
      " [1.9 2.8 6.1 7.4]\n",
      " [2.  3.8 6.4 7.9]\n",
      " [2.2 2.8 5.6 6.4]\n",
      " [1.5 2.8 5.1 6.3]\n",
      " [1.4 2.6 5.6 6.1]\n",
      " [2.3 3.  6.1 7.7]\n",
      " [2.4 3.4 5.6 6.3]\n",
      " [1.8 3.1 5.5 6.4]\n",
      " [1.8 3.  4.8 6. ]\n",
      " [2.1 3.1 5.4 6.9]\n",
      " [2.4 3.1 5.6 6.7]\n",
      " [2.3 3.1 5.1 6.9]\n",
      " [1.9 2.7 5.1 5.8]\n",
      " [2.3 3.2 5.9 6.8]\n",
      " [2.5 3.3 5.7 6.7]\n",
      " [2.3 3.  5.2 6.7]\n",
      " [1.9 2.5 5.  6.3]\n",
      " [2.  3.  5.2 6.5]\n",
      " [2.3 3.4 5.4 6.2]\n",
      " [1.8 3.  5.1 5.9]]\n"
     ]
    }
   ],
   "source": [
    "data.sort(axis = -1) # 简单排序 \n",
    "print('简单排序后：', data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据去重后： [0.1 0.2 0.3 0.4 0.5 0.6 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.  2.1\n",
      " 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9\n",
      " 4.  4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3 5.4 5.5 5.6 5.7\n",
      " 5.8 5.9 6.  6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.  7.1 7.2 7.3 7.4 7.6\n",
      " 7.7 7.9]\n"
     ]
    }
   ],
   "source": [
    "print('数据去重后：', np.unique(data)) # 去除重复数据 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据求和： 2078.7\n"
     ]
    }
   ],
   "source": [
    "print('数据求和：', np.sum(data)) # 数组求和 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "元素求累加和 [   0.2    1.6    5.1   10.2   10.4   11.8   14.8   19.7   19.9   21.2\n",
      "   24.4   29.1   29.3   30.8   33.9   38.5   38.7   40.1   43.7   48.7\n",
      "   49.1   50.8   54.7   60.1   60.4   61.8   65.2   69.8   70.    71.5\n",
      "   74.9   79.9   80.1   81.5   84.4   88.8   88.9   90.4   93.5   98.4\n",
      "   98.6  100.1  103.8  109.2  109.4  111.   114.4  119.2  119.3  120.7\n",
      "  123.7  128.5  128.6  129.7  132.7  137.   137.2  138.4  142.4  148.2\n",
      "  148.6  150.1  154.5  160.2  160.6  161.9  165.8  171.2  171.5  172.9\n",
      "  176.4  181.5  181.8  183.5  187.3  193.   193.3  194.8  198.6  203.7\n",
      "  203.9  205.6  209.   214.4  214.8  216.3  220.   225.1  225.3  226.3\n",
      "  229.9  234.5  235.   236.7  240.   245.1  245.3  247.2  250.6  255.4\n",
      "  255.6  257.2  260.2  265.2  265.6  267.2  270.6  275.6  275.8  277.3\n",
      "  280.8  286.   286.2  287.6  291.   296.2  296.4  298.   301.2  305.9\n",
      "  306.1  307.7  310.8  315.6  316.   317.5  320.9  326.3  326.4  327.9\n",
      "  332.   337.2  337.4  338.8  343.   348.5  348.7  350.2  353.3  358.2\n",
      "  358.4  359.6  362.8  367.8  368.   369.3  372.8  378.3  378.4  379.8\n",
      "  383.4  388.3  388.5  389.8  392.8  397.2  397.4  398.9  402.3  407.4\n",
      "  407.7  409.   412.5  417.5  417.8  419.1  421.4  425.9  426.1  427.4\n",
      "  430.6  435.   435.6  437.2  440.7  445.7  446.1  448.   451.8  456.9\n",
      "  457.2  458.6  461.6  466.4  466.6  468.2  472.   477.1  477.3  478.7\n",
      "  481.9  486.5  486.7  488.2  491.9  497.2  497.4  498.8  502.1  507.1\n",
      "  508.5  511.7  516.4  523.4  524.9  528.1  532.6  539.   540.5  543.6\n",
      "  548.5  555.4  556.7  559.   563.   568.5  570.   572.8  577.4  583.9\n",
      "  585.2  588.   592.5  598.2  599.8  603.1  607.8  614.1  615.1  617.5\n",
      "  620.8  625.7  627.   629.9  634.5  641.1  642.5  645.2  649.1  654.3\n",
      "  655.3  657.3  660.8  665.8  667.3  670.3  674.5  680.4  681.4  683.6\n",
      "  687.6  693.6  695.   697.9  702.6  708.7  710.   712.9  716.5  722.1\n",
      "  723.5  726.6  731.   737.7  739.2  742.2  746.7  752.3  753.3  756.\n",
      "  760.1  765.9  767.4  769.6  774.1  780.3  781.4  783.9  787.8  793.4\n",
      "  795.2  798.4  803.2  809.1  810.4  813.2  817.2  823.3  824.8  827.3\n",
      "  832.2  838.5  839.7  842.5  847.2  853.3  854.6  857.5  861.8  868.2\n",
      "  869.6  872.6  877.   883.6  885.   887.8  892.6  899.4  901.1  904.1\n",
      "  909.1  915.8  917.3  920.2  924.7  930.7  931.7  934.3  937.8  943.5\n",
      "  944.6  947.   950.8  956.3  957.3  959.7  963.4  968.9  970.1  972.8\n",
      "  976.7  982.5  984.1  986.8  991.9  997.9  999.4 1002.4 1006.9 1012.3\n",
      " 1013.9 1017.3 1021.8 1027.8 1029.3 1032.4 1037.1 1043.8 1045.1 1047.4\n",
      " 1051.8 1058.1 1059.4 1062.4 1066.5 1072.1 1073.4 1075.9 1079.9 1085.4\n",
      " 1086.6 1089.2 1093.6 1099.1 1100.5 1103.5 1108.1 1114.2 1115.4 1118.\n",
      " 1122.  1127.8 1128.8 1131.1 1134.4 1139.4 1140.7 1143.4 1147.6 1153.2\n",
      " 1154.4 1157.4 1161.6 1167.3 1168.6 1171.5 1175.7 1181.4 1182.7 1185.6\n",
      " 1189.9 1196.1 1197.2 1199.7 1202.7 1207.8 1209.1 1211.9 1216.  1221.7\n",
      " 1224.2 1227.5 1233.5 1239.8 1241.7 1244.4 1249.5 1255.3 1257.4 1260.4\n",
      " 1266.3 1273.4 1275.2 1278.1 1283.7 1290.  1292.2 1295.2 1301.  1307.5\n",
      " 1309.6 1312.6 1319.2 1326.8 1328.5 1331.  1335.5 1340.4 1342.2 1345.1\n",
      " 1351.4 1358.7 1360.5 1363.  1368.8 1375.5 1378.  1381.6 1387.7 1394.9\n",
      " 1396.9 1400.1 1405.2 1411.7 1413.6 1416.3 1421.6 1428.  1430.1 1433.1\n",
      " 1438.6 1445.4 1447.4 1449.9 1454.9 1460.6 1463.  1465.8 1470.9 1476.7\n",
      " 1479.  1482.2 1487.5 1493.9 1495.7 1498.7 1504.2 1510.7 1512.9 1516.7\n",
      " 1523.4 1531.1 1533.4 1536.  1542.9 1550.6 1552.1 1554.3 1559.3 1565.3\n",
      " 1567.6 1570.8 1576.5 1583.4 1585.4 1588.2 1593.1 1598.7 1600.7 1603.5\n",
      " 1610.2 1617.9 1619.7 1622.4 1627.3 1633.6 1635.7 1639.  1644.7 1651.4\n",
      " 1653.2 1656.4 1662.4 1669.6 1671.4 1674.2 1679.  1685.2 1687.  1690.\n",
      " 1694.9 1701.  1703.1 1705.9 1711.5 1717.9 1719.5 1722.5 1728.3 1735.5\n",
      " 1737.4 1740.2 1746.3 1753.7 1755.7 1759.5 1765.9 1773.8 1776.  1778.8\n",
      " 1784.4 1790.8 1792.3 1795.1 1800.2 1806.5 1807.9 1810.5 1816.1 1822.2\n",
      " 1824.5 1827.5 1833.6 1841.3 1843.7 1847.1 1852.7 1859.  1860.8 1863.9\n",
      " 1869.4 1875.8 1877.6 1880.6 1885.4 1891.4 1893.5 1896.6 1902.  1908.9\n",
      " 1911.3 1914.4 1920.  1926.7 1929.  1932.1 1937.2 1944.1 1946.  1948.7\n",
      " 1953.8 1959.6 1961.9 1965.1 1971.  1977.8 1980.3 1983.6 1989.3 1996.\n",
      " 1998.3 2001.3 2006.5 2013.2 2015.1 2017.6 2022.6 2028.9 2030.9 2033.9\n",
      " 2039.1 2045.6 2047.9 2051.3 2056.7 2062.9 2064.7 2067.7 2072.8 2078.7]\n"
     ]
    }
   ],
   "source": [
    "print('元素求累加和', np.cumsum(data)) # 元素求累加和 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据的均值： 3.4644999999999997\n"
     ]
    }
   ],
   "source": [
    "print('数据的均值：', np.mean(data)) # 均值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据的标准差： 1.9738430577598278\n"
     ]
    }
   ],
   "source": [
    "print('数据的标准差：', np.std(data)) # 标准差 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据的方差： 3.896056416666667\n"
     ]
    }
   ],
   "source": [
    "print('数据的方差：', np.var(data)) # 方差 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据的最小值： 0.1\n"
     ]
    }
   ],
   "source": [
    "print('数据的最小值：', np.min(data)) # 最小值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据的最大值： 7.9\n"
     ]
    }
   ],
   "source": [
    "print('数据的最大值：', np.max(data)) # 最大值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumPy索引切片玫瑰花操作：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(615, 650, 3)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = plt.imread('./data/rose.jpg')\n",
    "\n",
    "# 高、宽度、颜色\n",
    "img.shape \n",
    "# 615高度像素\n",
    "# 650宽度像素\n",
    "# 3颜色通道：红绿蓝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]],\n",
       "\n",
       "       [[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]],\n",
       "\n",
       "       [[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]],\n",
       "\n",
       "       [[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]],\n",
       "\n",
       "       [[246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        ...,\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246],\n",
       "        [246, 246, 246]]], dtype=uint8)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 维度\n",
    "img.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10a2df520>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示图片\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12e5bba60>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 红绿蓝 ---> 蓝绿红\n",
    "plt.imshow(img[:,:,::-1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12e733b20>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56+BqPoPWUNYtWZ6gSpjIFK0USunXF/Ab9v4irwcPUYRjQEh0jbuzc3SKLlf8kFgl1FWdFgvcxT3EKgaveH76HL+d/yZKDN2bPfKlImhkVLCNUQqM1jP1LYgxNH2DgYxx1lJOSyjCZ+GVQJISKaFynsKXVFKxELepRXXqUoPI46wQRxFuRrMHAR3IcojgvQtkNVPyr+Rfofc0f2n5L5LEtW/imfDth8PA8W0OEXhx/0V2/C5LepFalDF0hlJZ9ExCfkezojUxEbGKiaRCmYSzyRnet/K9NGtNhmrIVXeFa5Pr9H2XVtRi4iYoBG00VvmZ1Y6g0RitiKMYMZDqFDtxFJKjNBitwBxsGWQmLGN2NDOdu3jECmktofQVAz9AtCLRESvRKvfH93EX96KVxovjDyYfp+cHDC4PyH1FrM3rcUlrjFIYrfCzLClRGSM/wYnFOo84QHmcBaYh8EkchVgYGwpf0qsGxCahrjJKClIdY7GI0jOTohlTFoVTgnM+UPCVpjAFvyG/Tmu/zY+t/hhamTs2AG81HAaOb2OomYNWqXKW4iV0rshsxnzaZuLykH34YrZdERq+zpxp87bkEebUHGvJGivRGqlJUEpRqIJT0Unm1BxHo3VORidZjlYYuRFd32PD3uK222ToBvSkNwsGhnqSUkWO+XieUX/EJB6HWqaEzEMpFYhdPpC8jBgyV8OaCicVUz9he7RDx3VQKJpRi3W1ygl/koV4CaUUA9vnVf8qg70x01pJ3IiDFmdGdddAYiJQQqQ1iUqoKkflq7Alsxa0x1uQnsdicS1P1KhYqi/xfe3384eXPkVPhiTLKfF8TF3XcFi8dxj0HSbpQf1XAKcC10UrwWlhICN+2f8LTvZO8ujCE+E7egtGjsPA8Q2CfB0KzDuC+Dcwt0PREUpbkk5T1mSdrJVRc0327C5pErPnOvRcj8inPGIe4p7m/czFc6QmuVN7KNSYfbtLoUpyN8WbClGKk+kJlswiS9ESZ6PTRKRUvsQrz9SOeXLyNJ/NP8vI98FkzEUtVNuwki7RHfTZrLYRZcOexGgUIRNo+CbHG8dQNYWtHFVVoCaKp7vPkM8VZCbhdHKaYxznDOeIdIR44YuTL3JbNhDxqFQgDlsGhWCUoWZSDIZYR8TEZCbjZr4VeBiVR5zgC8ENLLVJjYXVRbqDDmYa8b5738/3ZO/nU/lnGKsR13fGDPSAo61V6qaJFkOhc5TYEDDE4b0PSmD1+u5NOcFEMcO0y7/Kf4W7y7topK23ZKH0MHB8nTgIGG/8/WtVY96RU3joFT0udF9js7zNoDXk4eRRUp0Qq5jKl3R9h9TUyCVnbCeIduzJLpfK8/TtgJKCUkqcONpxm1xyClVSVzWcFkylaJomK2aZtplnTs+zEi2zGq+wGC/x/e3v57HGY3xy/Ie8UL2IM45FM4fKhCPZUeZ6ba5MrpCbAu09ShkWZYn7F+/DJpaxHbFq1mnFGa/2z7PPPrGLWY4WuDe5l0f8oyxnKygPlTieKZ8hMobGQg1buTsuX5ExpCYKknyd0oiaJJLQrXqUtsIrcNZhpxa9b1g1a8w/0GLb71ENLO86+R4eaN/Py7dfYHe0h1UWnRoGwxESC+24TdM0yFQDYULpy9AJ0j60rpGD9hAOjxGPVoYL6UWe6j3F96/+wGHGcYivHkqpP1oU/Bo8NdUBcUppxAs3Bzf49OCTuMSSNuokKsUrB37GvIwUddvAI+y5PfbtLruyy8gNmbgJU3KsWEQJWmm23TalVFRi0aJwRoiUJtUJNZMSq5hW1GaeOe6rP8BKtAyiaMRtfqj5w5wsT/P54vMUdsp8tAhKSBbmeUftHdwY3iAm5ljtGPPNNn0G9KsePdtj3+/Tihts2e1QN/Axb6u/jTW/zqnoNBERooSB7XHRXSCLEiZRjnahLZqomFTH1HVGjToNHdihmU65Ntpgwc4RqZjEG6IsIT4bk9cnbPQ3GV4YcurIGd61/nY2d27zGy/9FtNyGoKA9VRpybSmEaDwBXVdI45iSldQiqXwNrSU7xgZCuqgDqIVleR8mk/xXvs+0jj7Rp5S3xH49wYOpdQ/A34M2BGRh2a3LQK/DpwGrgF/SUS6s/v+HvA3AAf85yLysTflyL8dIBKMexGKMscYQ6Tj1/14D8hQB0a9f/TJd1KMA4M75YXnbz7LL1/9FYoTIwRFJIaUBEFCG9GNmdgpq3qVetZguwoEMHGehm5SOksufQoKPEKkDZXYO+9aegcWKgWlKRnrKShhq9glIeZmtcG57DSJzkiLlIV4nqZu8+74XWzJFjk5RhniKKHeqnGudY5UxVwsLvHl6nnGfkhEzJpZIY0Tur5D4QpwcDI6wlq8xrgak2Y1DnT7rxbn2fXbNGnSMDWGeoISTxYlNEydVtQkUykKTV03cKXFDDTz4zniEwndpMtUFYwnY3oXe+SXCtK1jAfO3c/27i6/+6XfZd92QEtoYVuP1UKVVejI4HE4LE3XxJgYseHzUgc0egl/dGTwCqw4lI65pM9zY3KDu9v3cKd3fkCEm/313eoF8qfJOH4J+J+AX37DbT8H/KGI/IJS6udmv/9dpdQDwE8DDwJHgT9QSt0jIu4be9jfGrzOqBZQmtKXXO9d5xO3P8Gzk2dZqa/yk2s/wUOLD5NGs+LeHyFHhOcqNB6YlCNuj28zkQmdvEdup/zWv/0tPv7cR0l/oE7tRIJODaIEK2G/7cVxRp9hfX6d6/k18jInJ6c0FUM/pKocRVGS5yVeBJ2BTjTEKrRvD/QiBJHbgSmPUgqnHZvFNp2yRztuUotqtG2LVFJauo3zjoV4gaX6Cl4Cj2Tipny5fInbdgMRYd2s0qZNr+pTxTkDO8RpoS51jjeP8WTvy6QSc5fcx7sa70AUPDn5ApW2aCU0TZ3U9BFx1ExKyzSJifAiJFozmg7o9gY0xnVuuFuMb46RluD7lvKmxe5YzErM4l0L2GHFhz71IbaLbXRsUCaoc1UMTD1lr4JEIY0YFWumFCQmoh7VScRxZxIE4LzDqUCZ9+IpbcWO3uczw0+zmCwyly4EE2bvZ63qsL35erau38749wYOEfmMUur0v3PzTwDfN/v3/wp8Cvi7s9t/TUQK4KpS6hLwTuCL36Dj/Zbi4AqklGI4HfJLr/1Tfmvzd9gttnHOoxPFJzuf4D0r7+Ynl/48b196B6lJDy5EB69C7grO917hlfxVinRKqQqu+xvctBt0ntgn6idMb0yp6iW6blCJDs2FSHFCHefuhXPcchuMb0/YdrcpFxyCA6NZjOZo6SbTnYJiewp1hW4r4vmYqB3NDHneYDmo1J0rqvchMDkmlJSkPmbqJsyZeRb1Cktxk/XkGEf1Ea76q+xU21yprtCTHvPRAit6CSOKjWITIs/ETbBjSzwwLC8vc+nWZa6XN9ENw275/+bv8V/zQO0+nnMvzJonhhhD3WRYHHVTx+gIERhUA8QrOt0u/8HcD/Lp6SeRReEhcz8XL1xmb2OM9BxmOSI9rqm7Gk9+4kl29nYhIYj5Ih0o8xa8dYGNnypUfOCMLlQSkUUZU5tjlUPJjICmQ/YX6ZhYBUGesyV/0P4YlyYXeGj0EI/Hj3O6do40raHV69vW78bg8bXWONZEZBNARDaVUquz248BX3rD427NbvvfQSn1s8DPApw4ceJrPIw3CbMWm6DuWPSrN6SepS355Qu/xD+7+c8DKaoRjHz91LPf2ePD6iM8ZZ/mRzp/lp89+Z+yWF+4w0fo5V0+3/k8vawDLc/YDpnIFEmEqR1yO9skfn+Cv+U44CMd9F1O+GPcvXSO/bxHuVtys7qBXXAhE6kEnGNX7zMXtTl61xrRvTHj8Zi9/j6+tFQjIa2lxGlEqcIe/nVfm2A+rAHxCmcduQiF7TKMRoxlzLH0aOBlRAnLbpGXypeZknPEHOVYvI4Vy6bdJEtS7LSi6Dv6m0OyRsZgs8/udB8ShXbCNa7yD3u/wE+UP86Gu4nSHieOSBuWokU6VY9UpRhlKF3B9dFt9ESzFC/y5NUvcXuyjcqE1xYrVs4uM700YRiNUfMQ1+tsfXaL7lYfZUBZha8cOp6ZGUcaVQp4RZVZTNPgjcLq0CWa2mkIMM4hSoXWM+C0o3AlWiliZUijGkMmfNk8y/nkAq+ZVzk5Pcl9owd5qP4wc9k8OvDsQ7b6XSSU+0YXR/+4j+SPrRaKyC8Cvwjw+OOPf/UVxTcRcsf/UhANw3yI8tCstcEKn7v+GX7l5q+QRznqgGzYMEgq+InHdi071Q6/Pv8b+IuK95Xfw+9/6vd59J1vo7i7ImnGaAy3q00ulhe4OLhMb9inKHKKogoenTWFShQz428aqsbc3ALP773A+FLOeGGKWQ2WgG7iQnDT4J3QdT16xYBGVGelvkTaSuiXfcqywimPkYhm3KR01YzQNVsc7sCWJzAmtXjiOMKLZ+iGbJXbKDTFNOdkepqHs4fZdhvUdMrQD5n6kqiMyYc5G+Md+vtdpITIOHYmexCD9gqFUIsb3FTX+V/G/wtWW5pSw4tFXIT3DqckjG8gY2/apaQkS1N6kx5SdxyPjrBb7JEPJ9yON5h7bJ7ppQIzFzF9bsjkSgEE6YzECmKQWNCRQlJBW4VTHjVS2JFFZQalHSiF15oYg7czmwAVqO7euZnITuGUo6gqslnre+LHvKxe5dX4Nb5gnuSR4mHeM30P99UfpJXNB4NnZQ5kPN/x+FoDx7ZS6sgs2zgC7MxuvwW8MX04Dtz+eg7wWwIBtOCt8NzOc/zjF/8JUnj+wvG/QBJH/I/P/xM6WQdd1xDN1FleYeYNKgM3dLiBY+Jyfqv1a/zOh/83rn34Kh88/uf4vke+n72yw017g+1yh4vTC+xNulRjh9igOVGpxkQKFc/OMgdJPeOmvUVnsE9pKmITQa7x2axbaGZbDq8Q51ERjP2YoipY0gvcVT+HbkUInolMGFQjUp2S+oSRG4Wtjg5JjvIzMZkJ3h12JqDLzYTdapvK54jyHImPMWcW2HXbDGyfa+ObbI+3mVY5VbfEDwVdi5iMJwgevEK8J9F1HqzdywUuMa1Cp8OLxyE4L0x9zrQc0xODoNjPO4HsRkGzaND3Eyb1nIVkgU7RxceCP1LRHLcY3xqQv1rBgVeIAZxGOYV3HhKNFo9PFIlNmC/nGHZHYEpoR6gMiCKsgPMe712g2B8I6yR0qnCCOAk09mYbYhjYIWI942hKVo/p0+WV6Su8ffoEd7ceJjMHE1e/80PH1xo4fg/4a8AvzH5+6A23/0ul1D8iFEfvBp76eg/ymw2FgNN8pfsM/+UX/kuud26glOGZ4dOohsGbijiKqLwNhUcFYh04RdSOUInCxg47qpgoT/QByxlOce577mbHb/OHg09ys3sLW1pEB6k7ClSsIAndFdFhX60iRZxGVJTEOiKqJbiWf4P9XegS+IHDF4K4kBKrhkbFGuscG5NNempApjLm0znac3OcaM1TGUc/7ZCZlGSaglOMZUSucgoCB0R7j9Mh7Zm4AoUBZYirfWqqwXzU5lp+nUujK4yLCWIF17W4DqhU4UuHOJmJ6ASxUOqSq/0b1OdqTPx0Vt9QlLZgKVlkUuQUvqKb99ka7FBmJXq2eKXliQcR1lf0TI+sllKqkirxmEoxfbEMDu0K0DKbz3RAziMEEqM4t34XP/z4n6G12OJffuFfsr25i+Qe3xJ0PHOCjw5MityMIasRHz5vKQUpBJNq4vmUxajF7WoHZTSR0fRtn0IX7NX2Oe/Oc27nHB+c+/Os1dc42Ld8JxPH/jTt2H9FKIQuK6VuAf9PQsD4DaXU3wBuAH8RQEReVkr9BvAKwQniP/tO7KiEfa3l3279W25Wt6Gm0XMavxB0C157arUMbwQxs07rTLKuYoNpxiEIxODGjnhe8/7/+Ps42l7nltsM3plZOPlxwdiGmSmv0jr4YkSAV+hIIyaIumJJsHkX09AzCz5wA4ubONxQ8KOZ2U1TwcDjcodMg//GiDEIXEcgAZPGpFnK+iOryJxgXMReb48iqYhiTT2uERMzlDEQKO2FLdAoUp2Q+yl7sk/mMpbLZV6YvAzW48Ye27GzPcLBH/Ai4ARdGUSEbbdDMopI4gTrHaWqwGgSSZi4aej4+IIyqoKgTYGJDBhIx0mgsxtHpSyRxHg8tueQadhuKB1o8GHMpA9qYQ1RBEdWjvJnPvDDHF08wUuTFyh1SXWzwncsVaNCZwadKtL5lCSNQ7O2cigd7AFkGpS/aIjnIpIsRpQhsjFWSpx4OmUv+BnN1LYvZi9yfniBv+H/Y+5tPoC+o+35zsSfpqvyM3/CXT/wJzz+54Gf/3oO6lsNJVD6kgv758PEs0yhm6AShdaANtiRJWklM5o2IKGQ6iaWKImIahHKKMoE2q7F8dox+n5I6QqW4gWm9QleO2zpsNbNWr0KjcJDOPl9SI0TndKKW+zvd8MuqhZOWj8Ryo5Feh6fe1SkoKbxPYcrPBSClCC5vF5pmk2P90mJwzKMR+T1CbuLHbQCryzUE6ZpAaKZkzZd1UVL0KFMfYEtO0xNwVjGFEnOmdppFnbn2S33qDoOKtC1sIUTD+J9qDeLBAXqrNtQOot4CZmR8jTjBpVYKlVilFARZPxBsKpRCpyBWlpjzARMqDUoUURuVuupZl+gJhgJzfx/iIS59XnOPn435+4+w4XuBYq45KK9QJ6WyNjjvQ6fUyVIKeRS4lpC1kzJJCYf5MEFzXoUYOYM2VyKQROpmJSEigLrLE5CPcRJ6GJZBc9FL/A/9v8x/7X6exxtnvxOjhtvXebo6wzP11Wd4bdgTCOAP1B5ZvrOJyUadAZ2aGnqJpVUgJ8Z2IA4j59U6HZCFEdI07PCMolJsKokZ0rlK+pxnVKVjMwYZQ3e2tlVGbRWRFVM1a9QNUVUi5jaPAyCrmt0rLB7lvxaiex7xDt0KwIv+G6Ft0DlkWnQb1AIUsmsS6PACh6FLAmjF0asv+sI179yFdWGaDnBuxIWwGQ6mId5E7xLvaC1CmJ58VhKNFDP6txTv4vt3V0YeVR91vJ1gJsNdwofaAgiPrR/lYdKObSVwF6ttxi6Ec5XdzgzkYmwLiStSik8jigzmKkJ0ncNVjk0mnJa4u1sa6IJWRyGeC1i8d2LnH3kLOscId+bcvvaLV69+ArubktFhbgwukEqj9dhjozkDms9k8oS1xKacZ1Bd4z3gkoF0zAkWYrWBkRo6Sb9so9XM9czY0LtZnYBsFjOJ6/xm8Pf4m9mf4vUpKFzd0Br/w7CWy5wHPTUD/gLqHACi/6jX5xCh/Q2AnT4YkXJnWnsXntiF7Oo59mjEzowAJHCATKsiOsxJoloRg0SYqxYtNLUdUbpCtpmDmIYqwmlCKqyREQclWMMrg7ZjG8T1RI6u13SKEPEY4zBjRzFtQJ32yMTh543SCX4IpgPSy7ISJBpuHJiCQvDMyvkhmXppzBaHFJcnGPhyAL71zrItESmBhlZyrkK23SYRFMSCFFeq9mpLoiCDgPU9BbHWkeoDzL6pkQpj9g3MCiFWTGRIMW3oWPlOoJoIZ6PaCZ1nFi6tkesY0SVYfG/4XsD0CLY1FGfZAwZBYWuzAhaJrirh8BEWNgPRLTfPcfi/AKdpzpcuXKF/nCANQ6dKbIqAW3CcyqPr0AbjTfhc0MpKIRSVfhUmGu3GO9PKLVDp2C0oXAFsU6omfof2X545/Az7xBmH72Tit/3n+CR7qN8YOX7UOHkezNO9TcVb7nA8e9qS0b5GK2FetK6oxlRAlM3oVPuhT1yFIpqd/w1AVVTTKYT7lu/m/F4Sq5ymCjcsETPG3wC5UhIWkmY7q6FmtRo6iZFlGMR6trTNA321R59NUCqjMX+IsPukNvVBqqlcZMKlWmKqkSnQfpt+w4/BrECSeiA+PEsQBSCTAQ/EShlNq6AUDR1b8y0JIwI6Dhum01OHznLMB1RbJdI4ZAc1MQyagxprbVQNY1zDuUOmKcOEShVydRM6Jke73vge8hHOZ28w8ZkKwyvljJkOrNuj3iQSkGfkAXFoJ2mnmTs2x5WWQo7G0M5g9YzobtSOAndlSxKGEgIYV6BwpPNR+RLGpkKZsUQn4vI7qqz7o5weuckr968yP7NvXABaGlUW1P1HX5YIYWEmU9VuChoDRKFepN3gnZBuj+oDWmfnqM+cVSRpfIVeMji2ut6JcI0vJlEDj273YtHoRlHA369+A3uHd/L0fqxYNT8x7MWvm3xlgscwIwK7Hnl1iv8g5f/expZg5975L/ixOJplHds7e/yP3/yf+Jaej2wDvWsly9vkM9nmmJQkCQpZ9VpXt57hWKnotqyRE1Ddk+G85Z8z+FXHYkkoZgmCVo0dVOjEkukDCZb4bQ7x8XOJS7tXsCsxaSNGirywS9UKfzQolONd4IbOXxpMShMmpDGCUktwXuhGOXktoDchYlphQRhyuwqTCSoVL2+ZRlCZSxbl2+zdHyRjZ1NvPPYKiwick2eFURpBIR2b3DHCpnVqBhjbYnOFKtr69xz8j7m1RzY0EZ9uvcMF/LXKH0IBtZ6xDrKXoleMCijqUUpueRM8wLrLVW41HNn+zgrGusZVd46i0oyzFjjUxu2UlpI31ZjpRVT5BXZkZT+zhA90GzrHebuaqNTT2O+yXB/gFnURMcTtFbkGxVUBEp6GbY5YgSJfMg4XSDFCeCVZ5gOaDabNHydoirQcej4NEydFbPMvt1/A68uyBOYjX7wPnTNLqjX+NXuv+RvJX+Tety8czH7TmGZvuUCx8HC3590+fmP/jxfzL9E3FKMxyP+2/f8t2TS4O/8/b/DZ/c+SfxXaqGSrwAn+OT1BppSYZxApxqwELdD+7QB0brGDxzFlSnJmRo+cUzz4NcpIhivaOk2Sgyl5DhjmfOLDIsh0byh/cgcSiksDudnKX3pZq1ahe85pBDWV9Z59AOPc2b5FPV6E53ARCZsl7vsTLfZ7mzTu9Zn+GKf8eYktEUtMKcwNUJAVAa/4/C7jtHtCelcQnOhxnB3DNYiXiMuwrYrouXo9c9PBG9daEMbYSKOzXyLlIRNu8FqssrJ+BRHW+v89Nxf4rXJeT7e/zg7bocIcCVQE3RskLGnMo7OtB94EwfBQqtQZ3jD1tL5A72O0J/2iSoFWUqlHeCxcxb9iGZdrbH/sQ6qrchaKa5RcdNu0O92maRT1JwivbtG1AZfgLLgxiFIGy+IhEKsn7mcaSOoNFgHYMJcmFE1puEapEmCjz01MgTPn2n/EJ8afJpNv4USD4SWrkaIYoVJIlITBHuX1Xk+1v0oH1z6cSIdfzOXwdeNt1zgCFEdXtp/gRfqL3J89RiTxoQX+s/zP3/ln3Bs8xi//4WPUf+BOiZVtOMmsYnomiHGy50iqALIFL1pF6tKiAUzZ4gWYnzl8BPQfZg7Ps80mZIzxRCTRXWc97SkRTNtIl7xh9c+wfnxefSiDrNHlIQTVh9Y/ARhXDZN6A8q0lrGyUdPoSNFc67NSrxCx3eYc/PIrmb7hU0GVwYMyz7tZpvj7ztFlGmuvXad4aU+tgQTaST2qKYiqidUN0s6SY/5ty2QDSumoxLlfGixzjuk9KjkgGQWjsqLC9udxFBWJde4SSOqsVlts5PusKrXORuf5e7aPZxKTvDJ0ad5vvwKpSmoInBW0DVD6Su0QBal5FWBJ7Re36j10ErPirKhtkIDJFXUo4zUCyMmQFCwFqZi8bF5fNfS7fbRYyjbltrxGpPzY+LjCfGiCYVsL7hh6MYorZFYBXZprNCzICHW42Q298WBVxqlHJN4TFxGRFkcCqI6pjIV72u8j98dfAirqjs6l1QlNOMGy+kSMRE1XaPmGzxtn+aR8WOca5+bnZ/f3PXwteItFzggELxuDzbwiaMT7eO0x8WOT/Y/zfgrA1RbE92VoBLFMB8zp1shTY44mAgQMtBIMS5HuLQKJ3eqAwGoEaHmAYHptEAGwk5rl9PpKSwFp6LTnExOs1/t8vnOF3i59zJV26JLhTagjEF7Zs7bgTTVdHWmrsTulDRPLZLEKbsv7fGv5z7EO+55F0rDjfF1ru9cZ3hsDMeFZbPGicUjnP/yJUZFn/iRDN0zuCslLiGQzSJQmcesadyOZ/Bql/bD85RPlbiRD2KwbbAnPTqaqT6FO50RjcYVDlKD9orEZmyNd5jUx0zSKVZbcnKORMf58bkfY71Y44uTL7Cj9rGTCWI0lB4Tx0yGOVYcUWpwM3q3ngVqZy3K6DuFRhMrTByjFDRp0KzqdHyXxETUTEZ5pEK2FUkVES/GmIYme7jGSn+N4XYPsSocd6FC1ycB1Qbd1uiGRjcVpqXRsZnVWNTrX74iZB4IQ0Ysunm8c1RSkic5S+kSx+OjbPut2RZL04qatKM2ERFaNMNqxFhNydOCL+8/xZnWmcBQPWgnfZvjLRk4Sm+5MbhB4UpMHIXCVF3Rn/bJHs9o2hrStniV4CvLzrPbZA/XUPWZ8O3gsqBh7CeztqDCVZ4kDvwNozWpxFgN/Vs9nhu9yOnaaY6qk5xKTlH6ggvVJZ7vPUdZL8LAITMjfxH2xYIC7WmXc2Q6Yb/bhalnfnGO/c90Of/Cy9QWU4peSVkUDO0IG1WU2xWpS2l/T5uXvvIq3Wc7KGBaL8JYEq3wXR/29IlCZRCdifF7JdUtYZgNSR+sM356hMdhb4K/Kyxcsploa9Ze9c4HApvAMBnjlacYFFS2pKhb9qsON+ObHE2OsqLXOG1OU8UVH+l9jGpYoaMIBUzKHKk8uqHxsULbWTHUq9kYCAmzT0LJB600zSijGTUxXqOziFbZIteWu9N72JpssJlskNYSlFbERcRDyw+y94P7TKerDDs9dqe7SKJRqcG7kHVI5UM9wwZNimoqTCMiSoIJNBOhLCq89ogJEvtBNWQ5zlFpm6mdkkcFi8kC/bIbGiY5oablS3bKXSYyIULTNnNUyvOZ8rP8ueJHaWRzfIfEjbdW4BBCV+RzNz7Db278JioDHYGKDWIdPvG0luqsvX+Fi+PLKAempqj6IFOQLJjWyuzCCyDKUboKtEKsUOUlSS3BiGYhWianYJQO2XG32Sw3WamtIMqzYW/SnexzbXhzVoX3mDg4gHkVUnIVGbSFBxv38XzxIm7XISoQnbYuXOP0qdPcHm5y9fNXXp9TNBUkF5o/1Ia+0P9iL3RcANUTovUIuSumeKoM+/sCyEFyBwkkzQy/Y6naJem5hKpvw9Zr7MlWMypfBjKohPaqduB14IgoC5MkR0qPFWHCmLIqGCdjtvMd2nGLy2aVJb3Eg+ohnh99hama4CtBnEe3DDrSYbyBDYxaZdRMQBMsApUJk+CCtsWjUBgThaZLAit6gZqKsQOLH3vK1HJv/T7uXbuPV/15rhYXQQt6KabdnKPyFXalwG/60I4tFRQKZTR+6vEjjY8cEmuiLGK9vU6tqrHR3aArXcRDMSzZzfdZO7IWdDY+pxY1iG1gnWZRjSzK6PkeNR2T6ja5qyjIaUgb167YL7o0kxaiw8Up+Lbwbbt3eesEjlkod9bxoUu/R2faDSegdWg1c4ZSjt1yj2OLGWfmTrPpb0NDE9+rsLdKdDN4QzAbYxhCkVCVZTjBleCsp5gUiBEuX75ItJQimadfDrlYXOAdjXcxdlNeKV7han4Nrzy+lDCu0QTti8wIadpBREKHDsPRALdvMbFh3B+zdP8yezf3GN8eQaVQ4nFWISXU315HlOPyb17B7leBvzE7AaMc6t/bgIkmf3aKFD60HUeg64rYGh76mYd57TMXKZZz4laEihSub/ExIUAe8BIqH1q0WoVC7qxZ47wPbFUsLvbY0hFnlsIXVCbMp/2J4z/KY9Hb+KUnf4lBMsQsGlQSmLd4QWcG4mCTeLBl84CONcQar4PAbOqnoAKfwnsJZkZyDdPStJIWY6Z00y770R4bvVshBimDMx6VVqRJRuuUY/92CXloD0si+CTMqfHe40XjRShdSSUlxxvHONU4zct7L3Gtuk6SR4HP4afMR/MzQZzQjluoylCqAmM0J6JjZCYjVjFDP8Z7T9vM0TJzMAqfrZoxe9Xrp+y3Jd4ygUMUKKfYHm/x4s0X8ZVDIkOaRaym62Q6YxpP2S13ubF/nYdPvQ28cFs2SU9G2AWoV3Um+QSfOsSE0QCiBKncbHKamo07FPIqp5o4rB1jlmPy6ZSt+g4pCRMZc8Pd4HpxnXguxvVLfCX4vkeawVzmIMh5pbnMZaq9CjdwUNeMByPuuv8042pIfDMmH5WBpVl6Gg80WTu3ytaHt6i2HSpnxqMILMvyekHrh+Zo/jDYrsW+UiKlRwYevaYpxjlrxVFO/V9O8YmPfoahDIgXEyIBX9o7MnMv4fXC/FgCqUwHohmzzM5XDrxHnAcvRA2NtQ4a8FTxNOdWz/FDd/8Qv33pd/F9h1MKXKDO6zS0jVWiZ52Nmeyl8khmUECpKnIKIhMF8i6hAzZyI/SC4ezbztC73KU77PO0fgbrK5QOZj4qUhArvHI03tamVjXYv7xLYatAOXeCLxy60Ij1QVkrgeNRSUk9afDexe9hNBjSpM4OHbRSJMQkEmGloC4Z4g1ZLeN4fIR5NYfXYPHUVRMfQawSpBT23DZHq3WiOOWOf8e3abYBf4Sb990Pr+EPLn+ca8NriFXIBObtIlM1JU4MJ+dO8MTK49TzOrvTXeaSeVJJ8OKJWzGxMrRsA7vnsAOHmzh86REbgkWYHQh+6vCFw94uqTpBJYtVNKYNvPLs2R36fkDHd0iyiGg+CRJ2B77vsH2LHwt+7JhnCecszguMBNdz5HtTLk2vYlZi6u9qUDuTEWlNFBlINbd+8yaTm2MYONwE/IxBKi6QmUbPDiFRRHcb1IKCHPwgSM6TKGVnuM2qrPPAj9xDXItw+xUrZ49gxOCHHjd0r4+MnGUDBxA/GxMZBaGI0oF7YgvLqDtiZ7zDlzvP8KnuJ/k33d9DH4N1tYbdsvi+DeK9ZKZCLQU3dfiJw854KVIJPvfYPPhhTF3BRHIKKbGqwrqKylq6ky5jM2Tl8WXuXT9HraqFzGsWNJRRd2wgfbOi9oGMkz9xhvZSEw7o+R68f7097PFM7JiRn7DvdknihGONY+zlPZK6oWla1FQdLw4rnlJZVE1YT1dp6TmMjjHEJBLT1nOsqGWUV0xMzsf0x/n/bP0DvrL3LE7c6+xcXifffjvhLZNxgJCXJR+5+PtYZRETomZUGvLemFu1W9yMbnK6fprTZ06xZzt0i27IRCY5pRQgkOiY1MWMb+foRFANQ9QwQTmpCFenPFCufe5IT8aoSONLz4pboZSKgRrcqcynNmNpYZENd5tpNcUNHRQaj6WW1jlz5AQ7w220KHwB0neoDHq3u7ROtmnU6syvt0l+IGPEiL1P7lHuVfiRIMVMavPelPi+FD9ylFdzyisF6V0x0WqEOxNR9StkAkQeE0ecvuc0vaTL3mgfs2Yov5CzdnuN9aNrvGRepLxREJ9OwgI/kK4S5r8pmYnwJJCmgJnZUVgEVVFR5RVMc0bxmGuT68wlC7R8k0ExxpQEjYmedTtm4x/FhuXjYwWlx9tQl5qmQhHn6GnIOqJ6RGWrULR1ijE5a/VVTi2eYHOyTY9ueD2BMq9IEoiJES3oVcXS969QfdoGtW4CKuWOBF4pReUrelUHKxVaaYr9HOM1tTgjVsns0BWtqMk8cyQqZUnPE6lgYm2UQuuI2EdsVts813mOXjVmxJBJPOLz+Rf4T7f+Ez64/kG0mPC5av1tN/jpLRM4lIf9wQ7XL11F2gJOsMZztXsFfJCu6xjKIw6TaWpRRhrN45zDKEPRL7B9y3jqaZ+ew24Kk9sjVOpQi2k4sRsqZDJVsBHUq4ZoTSOVxnYtc/EcFSWlD6MHI6OZjKc8MH8vjeU6r/lLTAZjZOgQ0ayvrdFTA1SiiI5GqLrGb1lYibC7jsnqmHQtI3cFbtpHnBCfi8lfKaAP5B5izfz75mHNMr0J1TWFz4Xp01PS99aITmrsbQ070MrmOHP3KToL+7x6+zy9jS52UOGBr3zuy7zzg+8izmLGW2NqcYw7NWuRHtDDlYJIEaVh7IAIB0bmM9btbKvhAhfETi3lSLCjPZaOLjHcGUOkUNmMdGdnwrOYMFXOzghoWiHO4gdCOaiIFmPioSc+neKNJzLRnUHYIxmTj66zUltmvblMJikd26GyJeIdnggX9jnERNh5S+uuJvubfXSq0IkKNZVZJ8mLZ6pKUl8wKAbsXNxnMN/nVHaCxMcYE5O7kjk9R83USFSCFoV40NpgiFDecGOwwWe3Ps0krliqtxGpEeXQV33+efVLHN8/ztuWHyFSBsTz7ebe8ZYJHKLh5VuvcPPyTeQuUHMq0IsVIA6ZKLQ3+AVLpR3TfIwdVLTTBeIoxiSaqgq06SIpWH5okc7vQ/d6n/p9DapGNduEh1Raph4MuDEkzSjIv2NNt+oxlilGhYLaVI+ZDHIeWHsIlhXP775EuZ9D5RnND+l2O8TtCJuVmAWD9SWqbpBdi+0J8ZwlaiXoSFH2SmiDWdCQC9JQGGNorjbpmwF3ym2Ro7oO5nSFXkyo3xux/EOrnHj/cVRN8ezFrzDemuBHwcsDD2W3ohPt08pa2PmK07VTnN+8iFnTwWpRJNRR0GgTzbgurxO4DlJvEhPqBT5snXQiOOMY7PURK/iBRaqD+sbMV2MWaFAzzY0OVop0oeZrLC0us3Bvi4ma4vF48eRSMHVTCltiXcXWdJthMuFEdpT1dI2hHTEYh++CGSvVG0czalGdragNcpwWdKwwSeByaAw1XWNJzVH3TS4+fYmbg+scv/cY69EapbdcL29wLj1FFtUoXcGYMUobEpWQiMIhjKYdnu5/mVFjylG9xrbeZuJy2nGLRlVnJ93hv8n/G35q4//EXz3y10MN59usVPrWCRwiPN/5Cu6ED6zEmUvUQZdECZhWjNU2eD/4iM60T+eVHrWTtWATWBeYmvD8huX4B49jvqQpswpdCW4sEIGqCDwAJ7ixRS+mRJlBdMUNf4OO62CUJiKY5L4yfIUz9dO8p/UezLGIr9x6jnxYsHNjjxPpMU6snOCKvUaR3Q7muzNjHd+3VAOFqkV3Rj6K8kT3xIHteCuYDo2KIXZY4ScOMRIWYwR6V5G9K0OOawaux6s3B1TDCtsPNmJSBsGcVEJ9rs6kMWGqcxYeXmKhmCf/6ITsPTV0M/BPdASooC59oxGyVmp2vQzFZE1YqMFZPZDv7NhDP2hkjFeYyMwk8oFmjtEgiiRSNFp1zq6d4+y9J1luLDORKQUllgrrLVZKRjKm5wd0dIexg6qyDIsR+36fo/ERmkkDaSvmZJ5CFfTdgJEfUVeeWi3lzIOnWYmX8LEw0WOSKCWNE0qxlL2S/e0Ot67fYOnhZR5oP0hn2mWHPX6g8f20ozleyl9CK03NNGjQJJOMRGUUPuel/DV29Q5n4hPYmkVVGi2GoR/TSpvEvqSX9Pn1/Ld4tP8Yjy088e0UM4C3UODw3nOzvI5ZNGHvXM5ctZII6uBjochzTEdRTxsst5YZqxG7UYfO0x1MXcOSRgYVo4+NSe9LGN8zpvYfNKA3xU8svqiCktYHLgW5gFM4FzwkNsotMsmwlIzcZEb2gqka8/Ebn+CHT/8gbz/+OAt+geuvXkPmFDd71yl3LWeXTrFw9xwvvfAS1aZDH1f4PcEtOGy9QonCjj0ykeCbSbg/PqIoegV2YBEUyf0p7nrJwv0LqIcMFhu2NFOoRhZX+VC7KGbS+FJYaCzwyPc9zJX0Gkwci6vznJqchFJTfDkne28tdFQIWxNxHhWbWQdYEakw81UQrIQ2jJ/5jHrx1Jp1JBW6RQfTiENRsnLB2xOQWBF7xfz8ArIMD87dy0PZA2SqhuBp6SYWx1RyRmrExI1xeApKGrqOn1HVy7Jiz3boDft4JZxcOM5K4whKNMeNZ7fYxeiIdtJCVpmNg1Q0VJMIg0IRa2HiS/obAx6460GixYhXh6+iM80H2u9lPV5jo9jgfHmRtWiNo6aO9mEC3NXJNZ4ZfYWO2ud47TiNWoMtu4kYj3KCN8LIj0njBGsto7TP74w+xEPth4mjby8ty1smcFSu4nrvZmBMplBLamRrGbZlyaspdJhVSxVOCf1qgGRCciah6OYUOw6uBiFUcz1m57V91k+u4uoWayxxPSatK4puGd5QE1yyC8FOKxYXFomcwVlHomMiOUjvFaIUHb3Ph17+EI+eeYyzx88gy4qtfBP1imZUDXh+8CLxkYi1v7DO/sf2qeIKt2/x8wqbVsRLKbLvsD3BDwW/50N3YFkHQ6AKkpPB06N+Vxt1ygCWSCKk4ZE9QQ00rZUWaZbg8Qx2Bpih4Z633c2r6jzTvTHKRKRZyjyLpM2YybUJ1dGK+Ew8c/sSqsISGzUrbHocCvElijB/1bnA//DOkqqElXiVjdEtVGnwA4eqa4wxQCi5RtawcmyFcqHk3tbdvCN7HG1iCl8gyjPxUyqqwOuQKQUFFSWiHKlJKCUhVwVaK6qqCh6lU8cFrlDhOFM/QVUJcZlRuinbw20qqchNQZREzNXmaUcNtGjG+ZTdvQ4TP2Yn2sVEmmPJcb6n/W6Ox8dIXMTV8TW012SjjLIsYN3z7PQFnhk9w/H4GP/nlb/KptvgC+WTTPwY511oOSuFKB+GRhmNIGxXt8hdTqSj0J5VBywPeN27lG96DeQtEziM0iypJag0a0cXUCcSxnocFJfOINaGwUe1iIauU9N1CgqKWkG8FCH7FRKrUOjsW+pPNOlvd0mO1vBTRzkVWieaoBT55hS8CU7pI4cvNZLCysoyQ0YMfZ8Fs0DbzJPHFUXlAMWoGvHFz32J3Uf3aa5mLI0Wuf++P8cnX/wM9oRFNwS5z3Ps+DHs1ZLN7U3KjTD7Q2dhiyX7OQwE6XvUkiFaj4nnYvRahB9V1ObqRMfiQIWWCO0VbmJpJQ3Se2u42DOxEyZXxiinWL5/mUvmClWeh2xq5EhcGtqUWaBlly/nRCsGWgblwPUtOg7dA4+bOXTJjOcZ6ONiHXXT4p0Lb8cVjt37tjhyfI3JjTHTcYkdzFy2YlhaWaCat5yqneBt9YdwIgz9AO0VpS4ofBiwbbEUUpJLaNGWYrHigxUjOqhbleDKEt/3VGXJ1eIqk8URR2pHcP2C2/1telUfSxU2V0aR1HZo15tMi5zRzRF21xGfiGgu17l//j4ebT1M08xhnOblZ1/hUx/6JMV2zgv6ZR79qQd5JVNct9dZLtb4wZXv40hyhJd3XmbfdwITVimUnm3vInDOYgQqJ2zKNr+5++u0XZOj9RNkUqNwU1pxi9OtczTTJvDN38m8ZQJHZGJO1k9x5OQK0emIyhREVlOWJa5w6LoiasW0Gi3ONE6jvWaz3GRQgZqLUG0XWKZOyKcl5tqU5F0p5bBEOU3VL7GLjvpShkoVk0tjpAJXCZFTDAdD9hq7NFptEMVIjTmRnWAunuO8XKSsSiRRTKucl//tS6w+soxdFH4k+rN87zvex8e+9PvIGiyuz9Ncr+OWU+aOzLPxhxt0NvuUuiJejVBoosWIxk+1iI4bojMRtBVlvyTVNZKjaTCqEU1KhhKNzRy+IYzdmMpabNexaBapPVFjkA6R0mGaMa6w+H1QomnUa8StBBo5bleorljihxW+VLidCtU04EswQUhn3R2TPBQwn87xnqX3cCI7zpP5U5iGprnQpH6sRjHNqfZL8oklLmL0uqGZ1Hmgfj/ee7rSQaMxSjOwgzszZZx3FFIw9hOmTBmVI3JXIAgJMWmUUuQ5k6klrWpMOhPytOBWd4Px2pijK0d4YOVu3MDTvdlhu9NhUAyZTCeMqxFSgI6F5r1t7jp1mretP8Riukw5LhhsDXj5qRf4+K9/kk5nSONIwuP/yX3wiGZix8wNFnjf+nsg1twe3+Tl/itUaYWJDRgdgtSBM50RYp9yxKxw7thJXjDPsV3u4b1nThps7m2T+4oHOw/yM/M/zRNLTxDr6E7r+puynr5p7/QtwBsnx3s8at6TJAnWlOR5xdTmwYXKQWu+BTWhrmtcOX+NwXCA1SXeBA6BSnUw1tFBWj3eyIm2Ijgamgk+FyadKUsLC0hb8HcJhcqxVwuUA1s4royvc5bTzM23WTZLrEZrODwpMS+5VxgPHTqCclJw819vkD6Q8NEzH+H9j3yAu+87x1deeJ5yktNebrMwv0jtVMx9f+U+tj+/zcaNDUzN0H53C9Y0OlOhoEjoSJjYML/Y5mh6jKZuYowiI8UrFTw6fZjaPi0m7LkO6qQiNzk1X4NUYfMSOw6alokfMSj6uJpH18HtC9Wtiuisxo+h2rDoZggaum7CWSahi5JFNd65/A7eufoYVeTpVfsM4gH5tEBZTRbHmLombjZoSFDe5rZgnVVe2n6FTtnBa0hrCe1mmxWziNHBk3XkRpRSMfJDesWAwpYzHY3gq0AhF6C52mL9xBq+Y7l1YYPe7oDd7Q6jtQmrJwesLaxwdOUoZ6tz2LFl0psE/YxyqIZwZu0My41lhtMBL37ueZ75tS/T3x0zHUywKGrHNad/9jT1h+e4Xl2nOW7ynuW3M6z30V7R6XTZzrdQSuNnHSk9K1orpYjJuC+5h4X6HCPGbJd77Ns9vHh6dGnW6vTyIU+5L/Fa/zX+avEf8peP/BWiCOCA8/Hm5iDf1YHjjdgf7fL0+Fn65YByGia5i/NBwFVBNDJkrYzdzT3G1weBTxARiEgedAJSU/hCIVWgmY+fGVH7gSaYYMhrB5ZiUtJYqOGagjsRxgRIHCaCTd2UY3NHqasWdyfnaOs5vPZ8cOHHmdeLfG76efrbQ3RmqArL5Okwe/VL7os88K77mT/Sont1RK/sMBgOadTqtBoN5t6/wNHxURbMAs3aHJXP2XV7bLoNem5AjYz75u9lvblGoUpGMqTyJX3Vp5KKyoeBSw5Hjz69uIcfO2KdoU2gZXtncSOLLzwjO+KVyau41KHTYOfnJx637UK3Z9dRNUvMUjSzLQzq4cVsiR878SMsLS5ysbrClelVBnbIbrmPqxwjPyZ30Ux9G7IE6x3aal7dvUBZVsG1zAmmFpGtZWwnbVq6Ra/s0BsMqCU1Kl1RiQ3myNPgt+EKj9KaaN4wiSbcNLfQbYV6WJO8nDC9VuD2J+Q7N+mc7bC4tshSMh+Gg6spdliFbs+RBLfnef7yy2z2N9n8/26Sb5WBiq8V9bsMq//RURrnmnSKDoiw3ljjruV7MWi6VYcvbH+RUixGNNqHlq8ywTyooZrcX7uXrtnnxvQm42JMIQVKaTSKSiw+FZYa8wyqIaNiyC8PfpWTW6d434n3o0VmVoRvLt4ygePy/hXOX3iNnCm6YVBJkIJ7H7gBvUmPZtpmvDHClzPWgZv5VcQ6cAoaAlNBFSFLKbsO9dyU5LEZi3LsKCYFtfmUJImp6hGuFjgLCuj5Hl+ePM+p2jF2h9vcX7+Xo9Ex5nSbH5z7PhaY4yP7v8/O9k6oH2yDveK4kd+i3W5z9O5jDHcuUt4s0QsKawYMagM2021qSY3F2hKZT0gaGefSM8y5OcZmdKersWW3qKjIZcp2scvIjnEEJy+tDHbG6gzdEaGoRkg1M0CuwG4GL9JhOabnLwQXsXT2AVcKu+dRpeC6gqp7SGVGDhMaqsEPPfj9TNsTPjF8nl2/x6QYUVYW52YFwSAoCpaHDqLI4FSguJe9MgyeKgOtX6KK8cBRSY47aem6IWoKYzfBFSGw+6Hgdh0+98zMP7EtjWtVFG2DrkdhK7UEca6prlRUPU1/d8h4ecINuYXvVrghyBjmPtAmuhFx5fI14vmI4sMl050pKI2uQ+0dKfN/Zh59TKhFCTt2Hx1r6s0UpYSKir1ph829bZxy+Myjo+BzoiJFPalz18IZLm1dplvvImWQ7ZvYoFMTGKSEzFCJopbUAJi2h/zq6Fd5ePIwC7WFb8p6+u4OHLPIK87z5Wtfodgusdai2xAtG/wBl6MSysrS2+wFwdeMhwGgjUc1FEQadfB4PXNId1BeKTGnI3RL4zYryl5JvlzMTG6DXa3kgm4Gu72dcpvcT0hMzI3iBmfUaZ5YegfLZoXH209Qf6LBJ+ynOL9zgXzb4XcFkpILn7jAw/MPcvRt69z61AbFCyW6DbqloQ42tozMiCPNY6yu1Xip/hLLC8vMmzkUQiUWpx25m3JruEG/GNzRmYhX+GketCwSti3iJOhwCpCJx/cdfju4gJV5hYsqdG2mSdGzFvTE4zuCnwpul/C5WYNWmhOPH+fV5Dw7g12GxQjvHeIc/uDSOFPw+sLjJ5a7Gnezp3fwVhjvjoNB89SHgUtOBQVtFDN//xIjM8V2KvxUiEpNYmPER7hJRewiBMj70yDLHwtlz6Mii2pUgRmaKHRTE9+V4Dcc5fUKe6MKxDYLPrfU78twOIrnpkStlMmHJ0xenWKyiPhUQvKemPSBFLWkcVPBR0KpSjKVUIll3+7Tt31evXye/uU+ojwq1mijUJmidqLO0dUjXL14g53pVhgxYRQ61kgNIh9Uu9po0Ircl0Q+wkQxXgkXucTndj/Lj5344DdFHPfdHTgQSud4dvMZnhk+zfHTRxlOxiRxhLQ8/c6ASmygi3uFm1ToWONndGbJBd9U6EoCq7QS9KJB1T1+P7Q9KaB8uaD29iy0cnuWalrhY48bu6C0nApUKlwxSkVpchZY4JQ+yfHGCSZ2iNIrLJtVHmxELL1viS82vsSTk6fZu9hB+pqp5Fz+t1e47yfvRb/fcOMjN5lenKDaGtNUmKZhcWWRdtKAOhxZWifRSRgMpGTGHRmxOdmhVwzCqALvwYdpcL5woANVXDnwpQ+isokgY8F3Ha4n1JZq1EzKQBeBD0MYjC0OpADXdyAKXwh+JCRaUz/ToBd1mQ6mVDZwRSBI5LWaWRACWnnK65a1eJVHTj7MR3sfwfUc06sFbupCPWoW2JRXHH1kDX9CMd2eUt2qSFdTzp4+x40v3WBixph5jTkSExsNtkZxvqC6Xd1hpeqRx2ca3dSQKTCCuTsmUVC8eqCS9UQtg9QM0yfHZMfqlE/m5IOC9J0x5lgEixAtBhJcsVVwQp2gvtjA+5ntgi8ZVAOcs9x89gblRhXGdMYes6ipn2oyf2aO7qs9bl+/DZlCF4KpaXwKCDgEjQnHDqCFoiqIoghVCt5UPDX+Mj/i/xzRQYfmTcR3XeCYCZLDVsPDRy78a/7+k/8v9id7eB2umPWoTtM0EKOImwm2X2JHDlUqfOVDZTt3yDS4oaMUKtPopkG3FT4PrlQYj/RD8c0NHLpmwArVKAikfBEEYFJ6qk5JEqU8sfgYFuFo7Qjr6RojOyTzNV4YPM897ftoqRZxmqAeN6yfXufFp1/k+ktX6e4P2L/d5eK/ucTdP3o38z/S5vLoCntXOtRrTY697SjRYsTC0jxZlobCm/dhgJJ4xjLm9mSb7rAfMq3ZfJVQA5hNWlMzBe2BRmTqkQn4scf3haiKOPvEGcbNEWqqwc48QA+U9L3weJUolFXM0eaJhx/j2txNupMOVluUzOwRI42OZoOgmY1AEI3rFhx5ZI1dv0WFo9gqqLaqMNrBzb5fJyydnEfu0vS29ykvFRCDWY1IlxMePPUAT95+KvxfKodpJDRMg+ThFLUm2JFj0BlRlDl5XtwZ36Azg3hPfHeC73iK10qUA5Yhf2lK7a4Mv++pWpb4VARNhaSAVlQTj7pYct/Re/nRx36Ep4unwQlGFKnERDpmd3ufnav7+NxiWhH6qCG9NyU7EeMuejZf2sTFgjE6qIC1BN9ZLcEzJZoVaKMwAtRqS22YMX7ZUiSW6/YqxckCk80ovMKbNuzpTzM79gTwy8A64SP+RRH5H5RSi8CvA6eBa8BfEpHu7Dl/D/gbBJeI/1xEPvYNP/I/6XhnPwW43rvK//DcP2br9lYYCBSHvvwoHjEux2Fvn2tUqtBTHVyjrKATjYrAVxLcojCB6h2BSg3KulA81QpiiJYMfuLDYOQsFOSCq9YbFF6Vphjk3Lpxi59+20+T1TMiSSgpqLSlHqWM7IC1eB2jY9b9GrLkqP1gxvH3HePSrcvs3tglv51z6Q8vceYdJ3nsrzxK58tdGnc3iNY1C/E8iUqCl4e3DGTIrXybTt5hWuRh0lkZxhfKrBPkRzP385l6VSRkUb6c/cw9fuKRIaTLKe50hZ8xXsUyY+EqpJgNWFIQ1TT33n8P7/7Rd3Ozdh0Kwe7bUMdIFSYOXp5eBPFh1qsWRblZEK8mDFsDpuU0TIPftPh+yIzCpCshaSQsP7DE1o1dprdyyIX4rgSTGMYyYeHoPFEVIeLx3pPbHBYEWRXSu1Lmhm3mby4wvNXn5tYGbhiMnMQEf1eFJX04w93wVD2LvW1RbU1+OUfFhLEOycyHVICpw/fh0Xse4aee+En6ZoCaKrQz+Ai6fkBS3WTnxh6WguiowaxGREci4uUItaPZ/sIWFouua6QKn6c3Hmw477xTKBuCB7NtMNpgtcPeckyLKVvzW2yNtjiXngts/+Dy+KYkH3+ajMMC/4WIPKuUagFfVkp9HPjrwB+KyC8opX4O+Dng7yqlHgB+GniQMLH+D5RS93zThk9LYNcpr/j9ax/l6uAyyqgwEb4KUm1fyuvzUXTYS0rf48eELohy6HaE61dQAgrilRi9aHC5DWMXK6ASVIPZyMNQK5FYY0sXnLLz4F+JV+Gq3XC8sPMy0ZO/zeOPPobJNF4cSsXcX7ufmsrIpcBZR6YzTqiTLJhFVqM1Fs4tcvXYNbbLLcoiZ6/qoGLD2R84gxFDpBMyCWMmN8tttopt9kYdxt1RGD5dzSapHVyMKkLQmAU3pVRQxx8MdarC+EipgLFgdMTqE4tkjRrOOkbVOPh8TCSMbChDy3rh5Dzv/sl3ct977+e23+BS/zKj4Rh0kLxHqSGNExBhPJ0igPGa6qbFFY7sXIKIMCUHB9VOhQwAPLodE9+jOfXwSUa9CZPrE2To0Sci4rmYLElp6ga3r92muFLMlLlClWjkPsijiigZ0x110N2ISW+CZAojwfZRSoc2ES73qFVP9v4a7mPDoJDUQKJQ7eBD6mOFAVQiSMfwjsce588/9uMoo+m4fXbKHWJnwixcqZjags29bTimoanRK4ZoxaCMof/CgHxcEtVnNaPKI7FCzbbQBxP4xM/EfjN7Sawij6YkizHc8uyv7fPPb/8yP9f4v9HMGuDMmzYk7k8zdHoT2Jz9e6iUehU4BvwE8H2zh/2vwKeAvzu7/ddEpACuKqUuAe8EvviNPvg/+aDB4Xmtfx6Xgspmbkph/EaAYzZPNVT0/djPjCM0lASSVFPhu4IUHp1qSEMKzmw0qjcK01RhROSB+7cNRVOisDilkDtVfSkBA89sPM1Of4eHHn2Avu5zdO44aT0hEsOICZYS5RU102BNHeGYOc5xc4L74vu4XF7mSnqFjfwWW26b7qQTjgWPt4rKWsa9EcVOie1b7DQ4aqlIUCZ4ZejMYPKING9S5hWTaoRXbuahEfQp4kGVQqQjFs8uc+qh46RnaigtjKsxrnT4KcggzKiNajH3fO9dPP5Tj7F0bBGN4sUvv0S/1kfXNEkrIk4TMpMSaUN/NAzdkwlML+c48UTHgqq2Vw3QBFaXm3pUSxGfS4lPJyRrBlGwf7WD3bSYEwnJSkRjvsHRxlHqGw1e+MzzVOMq6IViyJ5IyBYzims5RewwTUN6b8T6uTW6r/bIfY7bdSgJGh3fd/hIoVua+L4Ee8MGf9qmRjfUjG/h8YlG7Ql3P36GH3z0+0nilIEfkrsJQz/Cikc5T+kqhtMhe3sdiAiO63MJicmYXp1Qv7fO/Pw83bKLnoswEsZX2NLd4eIcnLfiAx8lFKR9GMuwqKmv1omPxHzOfZp/cW2Nv37mr5FljZlr+rdgq/JGKKVOA48BTwJrs6CCiGwqpVZnDzsGfOkNT7s1u+3ffa2fBX4W4MSJE1/1gf9JOKhwVFXJvu2QtCOsCTZ5oZU384OAO6kcVlCxQnSoUUgJTAWMAWORSuFLR2RM8L1MDD6yaC2oVgzeB4KRdSAamSioBc9M33PhBPaCn3qiVvDnvLp5ja2dLXzTsfTQVbbKDVqmTZMm9zTvZcksIeLQHpI4ZpEFFqIFTqVneKcbsWk3uF1uhGxIxQzcgL1qjzFThtmQ6dKE6Sgn38spdguKQYFVHpUq4nbE3Jk5Hm4+zCk5TW+nx7WbV7m1vUEpFTLviRcTWotNlpcXWZpbIkkSIjQd38F5ixs4fNfi9mHhRJuH/urbOPOuMzSzJkWv5PKt62w9v4V6QpGtp7SyJpnJqKmM7eEexbjA7nnyF6dIpojWTKi5WH+nPawQ4uMRWhn0ksI0NWkzJn8tJ7+ZYxZi4hVNul5joTVPvJPy9K88xeDWEClB1RXpPSnxyQStFLbngvFwBSI5fsXTuLtBtWGx2xV2BNGCgZ7CdUJAtDcr/FCIFmZkOiHUrQaCOMvqo6u85+F3Mp8s3Jn9MvF5mI3jQ3E+91P8lifPp+iaMNeY59j6UYiEKI5YXV9Bbcb84Sc/jqsqWDSoeY3qzKwUaxEqMmGAtweY+XP4MBhdtwXTiCBSVKri16vfpHG7xX90+j8MLmzfsNX1Ov7UgUMp1QT+N+Bvi8jg/6Dl88fdIf+7G0R+EfhFgMcff/x/d//XA5lNKzfaoCOFqZuQfjqPVBrxYYCyzI5MPIFrcbCNKUKdwk+rIDZygsoMJjX4zOGiYJ0nqUI3FJLP0nsEo9RsqLJCzVipMpMgSSH4Rvi3Lz0TmcBAs5fsMcpHLGdLvO/k97Dnd9ir9smihJpp0rA1GlGDpmljvGZBz7OWrPKO2rvC/9ErSgrGPqdgSt/2GMmQQkqKMzkjN2Zjcpvzt19l4/JtRs9NyBsFk6NPceXoNRaXFphbnqde1DGxYa7ephE17sz5UDqcqoWbMHZjJsMpdtNCV3P0gUXO/vlzrB1fJSFhYsdcmlzmuX/zHNXEsfaDa6zV52lFC0Ri2B7t0usNKK9UFC/lSAZROyg/ldFhoFJZ4KKg0lVzCiYenZrAv8EwvjlGZwa9ponWY9JmihnGvPrPX2b/1W5gYi5qkodSzHFDvBDDAOzIhlm7BK8UmzmmzQlRLaJaiLC3LFIDs6Jxtx3OK+IHEnzX46ezqWxjhYws1BWtt83x0CMPcCo9SZ06hRR3nPSbUZ1+OWCSVxTa0buwhysc7SMLPHDPfWykG/jUs9BaoBG16Pe7VL0Dj9hw/qbzCX4QZttoIG3USIjC9ld0GNGgHFXLh5O4DKrjUhd8rPsRfnT1x1hqvjm8jj9V4FBKxYSg8asi8tuzm7eVUkdm2cYRYGd2+y3gjSnEceD2N+qA//0IMUijqSc14mkSJp9HYQEHb1DudAKQ4A/hD/aQjvAFjD3ksyq20/hJ8BFVceiwEHt0w0Dkg+uXncU+B2JCdwYPfujBzjoYaTAPkhmNPQQtR3F+ihQwfnjIJ/Y/SaxTWnGDLM1o6CatqEWmEhq6xcnaSRqqSY2MxCezeaowlTFWPEbC6MQFFjHKMKnG7O7s09/q897j76VzqsdLt1/k6ivX6Z7v0bvRI11PaR9r0W61WElXaZo6VlVEKsJg8Ai5n3LbbnKtf5PRrTHS9ay/b4X2I3O4rGLEiN1yj0ExINcF5h5DrCJWl0KNpqYyrvSusbF5m+nzOdX1EqmFQmkYNx+2cnZqQZnglhVponYUylZmpgG1mqqsUIuKaCHcXzc1Ov9mj53LuyglqBVNfE+MXlREC4Zau8b0qRwpPMoqhFmHomGxGlQLTHtmOTgEswjmeITbseiGQbcNxfNT7EY4ccyKoX5/jbOPn+Ke1t0s6WWU0kReExNR1xlHs2NMJpPQji1KRnrM4vsWeMfpd3DlS1fpRl3SpYyqtcd+uY+NPEZp7NQii4rmeov7Tt5L0ol4eusZvEAlFaZpyJIUowxGGxJvyH3FcDzAGY/bsVS7ju1sh+6pfRabC9+ajEOF1OKfAq+KyD96w12/B/w14BdmPz/0htv/pVLqHxGKo3cDT30jD/rfc8BhXwe4yuG9w2uPyQw+8zPuQdjL4gORyLs3BA0X9vhUs5TEBvae3/eUaQmRDhmGE8x8MNXFStjiVIKPPJEPowPVzAndVz50HnLBWEFnYbtCNauVjIXJ0yP8LUfjkTrR0QmDpIcaKuIoRXkVTpZI0xg0mIvnqZvAGvTOobRh6PqkpEjPke8WTLpj7Ah2dvbo9PcobEUzqXPu1BkWz87TPdZjMD/C7VqmF6aUGyXdxT6bK9ukcylZLaWW1IhNBKIoXEF/NGC8MabasTQfbeKOeXqux2A4YMNtUVQlfiLU2xmtx5rMN+dYT9eomzqXd69w/cYNRl+cYG9X6HpoxyoDIDP38fDFiQjOOmITobSisdCg1PlMCAY+8qhaqEFEaYR9zXLrqQ1ogaoZzKLB1BXRkiE7kRF1I0bXhqEMlYAyHqaCHSuIPXE9wjQ8pq3xI4+qBRc1Xde4sSdqBwNlt+OIjkek96QsPbzE6dZxFlVYmB5HpFO8D8bVZ80pXqxehkgx7U8x65r7Tt/P6OqQ6+MbqIYw7I5QG+Fcipci9ArEjZjFRxe5/9i9nM3u4mLvEiIeHWlUDFY7Jn4STnWn0EqTZTWWq2X2R12q0iPdEnvcUvryTVtmf5qM473AXwVeVEo9N7vt/04IGL+hlPobwA3gLwKIyMtKqd8AXiF0ZP6zb1pHBQBBlCY2hrvn7uWZ4su0ldBhgFQO5xTeuTDFfeZKdUAqwgXas3fAG/wyfKVwQ0+cptixRSYuWOzXNL4/MycugvcG8zoEkYOXB5hNWVcTh+QGlSmUD96fEFqf4hX5xTHT8xPi+Zj2fS3MkRiXThHtGUVDdMPQa/XY1pvoyBB5gy095aiiHBZU04pq4CmvFlQ7Fu2BOByLEhjqIV/pvoR+FlQCZAo1p1GRprplsVuWql2Rz+eM2jpsDVIdeCojj71tcRNLcldKFVnsjsMXflawAzuwQa9yv2X11BLH6kdp6Abnty9y4/JNxp+e4G47aAb2I7O6H0Io4GZhdIH4MJvFeEFnGr0F6w8cYafcDWzOWAXWaqrRY83mR7fwyqFrChqKaFURn02Jj8fUoozep7u4jgu2CXVQMUihYCJIJrjIIbFgliPcTo5PQ3tW1Q10LW4sxEdiVKRI7k2o3V9nsT3HXDQHSjGlYF6lDKsRPd9lKVrl+vgmo/EIMoXdtjSPNJifNvjs+S9hpYKBgi2P7VlMO8atC/FaQvueBmtLK6zIKp3Le1y8ffENW2oJF4rZcAIRhWhhzIiqUTGnW0SLhuhsC1/CSA0Pin7fcPxpuiqf+z946x/4E57z88DPfx3H9XVBSRjS/D2r7+HXbv8aUzMlMRFVUiHYoE+I1R17v9DiCmK20PrizohDlEfXI+JVQ9zQ2H2H9D3UdTDeHYTp8WIFJQTHcz9rbR7shxxgQHLwhUdXM8q6hPdXntAmlpCV5DdL8it7qFwRJwZTi9CpJlusMYoLdEvQZxNUEizxvPWIFog1pgHpOxOScYK9WlHerGYdo5mXn3JB4Jcr6AHbgGhUHCjQbtdj6wpdC5wUlagweLofhizpVY3vWYr9WStXgj+oKIV0w+PUWcVSuoj2mhe3XmXr0gbDj+a4bYtqKXQYK82BubHSIUtUPvhSaBMWhvMOUzP094fc4+5hnI6xyqNjhY81Sikmz02YTnLUkkLPG6ITMfV76iwem8cZT/FqztSW0DTI1OInBonAJLMssQgEK4xCJbNrxUhBIrNNmiBj0HVPdMKQHo3J5hMapkldNSldyaa/TSaBkr5j99kb7PD8xktYbaGrqHYqspWUC5OrTF4ak9+aoJSGmg4u6rlD9QR1NCFr17BDz5effZat7W3StQSfC06HrpexCrKDbJbA89CKSpVMG5p6lFFREhUGa9ybppL9rmOOqplVv48Uy7VV6j4DI6jYU6YVLvO4UlBRsK7DByYisyAyK+mHiVpK0IsRZiUiOpEyvZZjbzrINEp53K7C990dvQYKdBaKoNqoWQct9ONVOyxKP/TI/EzfUYX3Fg9+ZtMXjidMZPMTj8od4kp0YRjuF3itcC2H2Z4SP5JiFkxYeE6FEYY+nEhmMSI6ZkjzGvampbxU4rYrfCmzjGrGY5nRoiWfpfBawTBsuZQJ9RNxghJBLxh8F/LtKky/W9WY+QgRhd+vsLseepCWCUYizt+6QGejz/jfTLA3A7lJzVijKg7EJl3TmIUIs2SCJ0pm7hDRXOHQDYN4x+blLc4+cZoL08tkrRrjaIRMPdPrOWZdE60akrMprWMtVpZWOZUdp3t9n2defQ7VEKI5g4piZODwfRvarZHH6/BZ6KZBLxjiozFu4kNxu6aJFqG6FYqqqqVRTYMxhnndRqO4VF3mVnGDYW1MParxwuBlunYPlmDVLrPz/C7WOqx1FL6gfq5O3svDd5aCyhQ6Cwmu3arY/tgW2/3tYH7UNiRFjB867qQOKjjg62RG2TeB8q80VNrSMBnOGSyeaTWdMcC+xe3Y7wQIhLzcwhc3P8/O3i5nF06j5lUwmk0tTtxsNiuIVXf8RxXqdWduLaiWRi9p4uUIcQ57u6K4WsFESB7Lgi6jAIn9HWGRamp8xwGzjKQKYrGoFuOLcAWS9dl7VYEZKCVQEf49I5ZJLsjEUeEpx2BWLenSbLVFgss17pmC+JghPpGi01kbuQr2fEp5ojQiXY5I1mvE70xolE3mR/PEWzF7N/a5dWWD3n6fqqwCFcUQdm4+nKSecJvSAm2N73iyoymtJ5okywmD3piqU+L2PPamwBbQ96iucOPWBt3rPSafyKkuupC5REBM0IVkYbuh20FgZpqaqPa6T6kgiCiIBJ0oNjpb3De6h5Zp0j7d4vzN85Q3bWhprkQkp2JaJ5scmz/C2dpdHEuOcyu/SdV/BqWDUlcnHr2g0e0Et2Nxo0DpPtDsgBDfm2A6QbdDKkTrKViFL0KhtDaXspouYpVl221zbXqV28UW29Nd0iijN+3NRikIrgbtVos832dsx8z7iPYjLar9ionL0Tqwb0VrtFK4wiEYbLdANzSUCis2cFmMvhPM1QHfRv7/7P1ZrGZXlt8H/tbe+5zzjXeOuDFzSJLJTDLnSapJVaVSyxqgkuSWbEBtdAMC9NKAG90PLfnJMBoG1C8NA+1+aD21YKEhyZY1QLZUkmrOrMo5K0fOwSBjnu74Teecvffqh7W/G0wNhXSLmVDRdYgAyeC9we9+3zlrr/Vf/8E2e3g77FyZvBvX0DHnuD/5sT1nH7jCUUz4WcaWf373VzjNc968foNnLl6z3JOFYRLiHCplLoEynuiTHJBGCFsBqQUN5unQ38rkm9a+xjd7ZKBo45DeEr9cbbN7JuMllJMaUEGGtqbVuRobVcU6iz6XomHjDoU0pq11AQrIhuCuCH0S46H0BgQI0L+e6G/MqPZry3HZMZ7J4PyAy5cucW36FHthh4Fv2HAbVL4ip0zKiflswf23H3D/rfu4gWM4HbKi5e7iLo/6B/QuAmqsgSzUuzWjp4aMRyPSKjFvF7QniXgjkt7scQtHOFfR7yurWzMWv7Wi/9bKfv5NB0N7X10lSCW4obdfjTsr3E1VowJ9Mts/suI2A/3jjjfeus4nPvUxvhO/y3S4xaObD5AtT32hYnptgw/tfojnhs9xrbrGlmzR7Fb4pSPmhKwSaeAQFLcXqK5UxAfRaPW1osd278gg4TY9Ikq1NSCtIoOP1sTjQLUTaLYanPe00nO0OiK5TOqVU51zupyTeqUae+pQ4SQRnqqoloFeerrQkc4ltn5mm/6V+4bViLeVa7KNGANs7FuBbAl5lcirRHIClZH41iE1EuTs9hURgvckEpMwLqbNsx/bc/aBKxwmohCyRlZxicTM8fKQb79ybHwELeKtBlgLgIR1GNnZyeOnHhnaAa+t0t+K1jJ6wyzyYUT2yojiijiszyx/b4nfEftz12vapPiRK2nymTwrs+eaZapixKdCPtOIdR2dokNhcE0hOPLScBhhTT/GxqyF0h2tkLcc6amKwYtCmkQWacYtbvJO8zb11oBnd5/mXH2OaZiCOEbTEXsv77Lz8g4bbpOOlqgdz/MM765u8fbyBierE6Pre6GqKgb1AIlw+PCU1a0l8ZUOeUXxvSN7oblaEXY9u7LF4atH5NZo2gwMV5Kq4BrBMBRXfvnG/EAFwTub+3LqiH1CJg55CDdPb/LC8XPsDPY4rW/gcFQXPDvP7/D5/c/zidEn2HSm10maGY7H1M2A3GWcGIkvLUHnUG2BP1eRZwlphRyjjQ+xxEdUUG0EdkabHHGKug63Gehyz2k/My6OKLNiEZDaZGOmCk6DjRBOaJueMLHQp1Z6mlTRb7eI96jPMIFqp6a70RoLtFWkzWgjpOOerhHyCsRl00qtb9ao9nsuImX7lFHbwIgQQk1aa6V+DNcHrnCsmaO1H3C5ucTXymo0x4RkzvQYgpr5cOH+i4rJoAtFV+riygSwtBR1N/DoSE3Alix8mU4tS6WAq/lhojpfG0Yyt4efWnBTb5hBQfMJppshyRnGQvtkVKFfHyXgB0JWsc5kXTDKFmhtfSjJkb3SvdLTvdXjJktOdsyPEy9UFxxvfOQNNi5MGQyGVNFTLxpSl5hcmrK3uUOserIoldQkEqMwJI8yi35ZwFxlMV+wmq1Y3Wjpv9LD70U2ho6jElXvrnqSRnhaePEvv8Ar/59XiKOMq11xurK5XkZmjuSH3ijwtScEgyMNdkiEypNixG84OidkEl+/+03+Nx/5RR5tP+L00oyt5zf4woXP84nhx+lJ3M93LfxIGupp4MLPn+do65CmHhAWgf52y8HNI1KX8NNAXij5yMBRQhlZQ8Ztedr5ir0ru+wcOg53T6w4RKVzkUerA1ZdS5d74sKwI9c4XCNkVzTaKZvCdWhjX5SeKRN63xMmntiDqx1aZ8L5QD7M9Lej3cEOpDYCY26U3BqjVsSdxY3m8n4iGcnQ5RYqpU+H1Lmh8QPOpBDCexxf//2vD1zhACN6Bee5ML5oM6EvJ//A/DW1Kx6Uag+tFrBQTNltnUGG3ANdMp0KAs52/Cj24BecQtvCFHWCbhqIpklIC9u4uGkwH4WCg+SFIsOSW5IM5yA+YbCyHlkCiLfVoQ4KaawAqmSx1x0thoGUbU3nM9ILuU10B8kUvA7iLWH1esdsb4bbcFZEDy1vRoJS1TXVcxX1BY/brXDBkfqEeIuLEKc2hnWJ/k5P98Ul1Tda6jbTDJ1xY72SsPdjnuec+4VzfDp/km/9+nfROkJVNii14KcePzEpu/PWZjeuwQHRkpgAxYmzEdADGU71hNdO3+SFveeYy4znLjzPC9WLnC5m3JcHtLrCi6OSBrKjqSv6rkcnStioqC4EdkZbHN4+IoupYeNJtLXv0O6V3CvSZ9LScdgd8vTOUxwfnwLGIp4tZhbxEE2+YJsNu/fECd6ZrUNW49kQ1qtnZVW1NHmAXHUsbi3o+2QdwtSxfDOSDyJ+t8LVIANnI8yGdRQ6N7zMRetQcxRYitlfBiMnxlGmGdY8Nb7GRdkvHbg9Fe/navYDWThAeDi/xxff/SKImO1aFe2BrBxosjFCFL9ZoVUmHUerDSi5sm1DPrb2NceybQCoBAa5bF6wLiNiI0cNfuKgVnQeyYsEHfidsndHgITOS8uesaLR26ij6w6iM08MGpBaCUFYrEq7tCzf895RpcjbzTvEKPPSGN8he3v4iObQ1T7IxgKIJqunomSotrhvQhxAfz7QbtS4sRDOBdyOw4/EZN5zRb64pPmdJZUIPhTwVDN4R14l4iISsmeR5zz3555nujXld3/zK/QhWfFoDPOR2ohVdV0x8gOT5DtPzpmYEjFFcA4ZGlnM3j7ljfYtfmrnc5zbOc9sdcI/fuMf8bg7wG0I1UaN1EJ/EpnfmrF9cZPqsKIP5gWiTcY/EwjHjpgVFsmCs0KG7FEFNwS/a0Bt2/fU05pmNWAup6xOMkq2jsAXRC0YruSCFUEFUl7roQTvnWXLAD2RSRjQjTsG54fkBwuURHcnE9/u0AF4r8jQNji5jQgZN/E0V2vczJUVPMgi29+Dos7sB4kRaRpO3Qnfle/x2fbzjAYjJOmZ9eD7cX3gCoeoGdd86faXeOP4LUstH9iNmk+zOUA7zJYtgN93pFPFEWy1OhCkzebLMXJQldM9GgcDNeKXq4y2rL0BU3iQkdnAEYQ8U2gVjYLbcdDagy/BiF+yXJf/9eghthpNZdMjGWpwjVAPleWRoq15NdCv+Sf2ZaUPLniJFShdgAys+GSfC/K+7qqM3KYZ9DjCBBqfqFrwLnD4aiIuF1aovJbuwLF1OTN8nIk3I4uVMOuFzWmGbCQxvJJaJZ0qi2WLGzlute/y4i99hD+58yf4tV/7LdqwRGpbW6+Nc0QcURKC0GopGNj7ah1ReUjLalFz5HvtKzzfPMf37r1CGDq65Yrh5pjcZLoU6W63LA+X9E1k0ozo7rZ0y4yfetIgES5U9G+2xONifYBDKyXsFSvA3YCfO/ouskhzBouGY47QaIeDydUFXztUbf0uXstrVnQtZxXrdOm0WBdk2ioycA3HzSmjzQHz4wXxoCV7wYnxSvzE4RuIc7VuKyl9mxju1FQnEPserSyVUOpyT9amy/JBWOaW7w++xzdmX+Vn6l943zeyH7jCoQJt7PnnN3+Fzq/Ij7PFPm4ENEd0kQth1MDMPDMijZsIYa8m9yaj952SDs12jwS6tFFBXJnTGxDvSK2d8tSC27LOQkTIx9m2ImXGJZcYAu/QZE5ZZynvwNpIx7Yt2daWAj4IzmXcSIgLrNNY2wNEa54kFw5KBrCQZnrsJK2BsUMm1kaDQCzxSN5WgXmVYOTwjbJaZLp7a6QomR/pQaZzEG4pzcTu4/FECT3M5o7Rhm2kRBx5nknHZi68qCHFzJ3+Di997iX+4t4v85tf/G0eVPcNkx44fOMI3kKdY46Wt1vS3nJ+4udGcBCTMXnVtDMP3D3Os8Nduc/5K/vkRcIPK+Zpxrw1innuEqvxklDVrB4uy4HgoVLy/Uw6yXZAhIK9BEX7BBoITWDxeMXN5i7hwNEuOoudwLZB+EzKxktZU4XFCyKu7PbKyrQyQqIUOUQnHRvV2O6Zi4H6dMDy4dLuS1cwr7Gz+7MzF3Sc3Uttu2C0PUUP1cbq2hkXZuIIY89w0DCoB/S546A65Iv6JT7ZfobpYON9jU34wBUOAW4dvct3Tr9rP12AECvcBegG0N+LpEdmeKsC6UHG7Tma3ZrutCvzYMZtB6OCn5gs3tWO3MjZw0nZEMhC0ULCoaEArZDmCe3AbwsMBZ0VQDSayjKvDNA6o6Y7BYxTkDvrNkiQWli2QloWcHR9JczoJZWVXKKg+lao3J7HnXf4TYc/5/EXvOXfqhWC+ChZjMEdkF6YbUAQpT2irIuN3sDagVyERYS0smkt9sLeDvghLKIl0SnmFpaOInHT0Y0E3zjuzO4wdA0ffe4l/rNLf4Xf/OZv8gN5ldxHUgcL3zL1I1QzSdRW22Lvr6K09yLhvAMJaG8gd85woifsXThH/XbgoDlgf3Ce9nbP+fP7LPda+puH9hA7JW0k4juZULRF8X6iu7EyXKgcJIYRmb5IVYkxkWeZmTuFpUntozMD57AvhkNIKNs6wa8HASnFDhBRslhxEb/+JWYl0AbyODJINQcLxTWUAgHUQnqcyrbNiskarmi7Jc2kpg09Mnb4bU81qRg2Ay6M9sm98qC9z4wZb1Sv8uriFT43+Pz7GpvwgSkcT8KXhG8cfINDPcCPKmRP6W+2DBcDmnGDXPP0riUeJFiYubDfHIC3thgVpCreEJ2Sk8ONQYZiqP9iPZ5gM3rBKmRihCZdYuPC0r7fXwim7GyT4SIlp0VXBepeu4p3nHE3yka5PLyZXhzMfUHgkiH/DmOwevs6AaOcX/RUTwf8jsdNHTIxLIEC/Dkv+Euemso6lplxRsTbWjdc76l/0NHfSOanOqSAyJA6ZdGJFZasrObgNyDXiTwUgvNmgnSSyUeJvOVJG8bYfe3kdbIqHxt9jD/507/Ehx99mC8//gp34136RcdcIDSWF0sy7sigauhOe7r7K2Q7ELbAS0WKSu4jfaw4rY7Z3Nri9qM73N68Q73RcPzuEbmPiDqja88VNwSWif5RxreB1W8syS24sR0CVIUli5aNmuBSGVGXCXHOVrcOuoMW7ZWwv9YOly7DGRPVlVPdOYdzzkKvsxpPpNgkEpRqEBiOhvRdDxXGpm1so6JdIp1Ypqx4yqiJAdSS0RH4YcBvefw4MBgMODfY45w7T5bEPe6Rc2TuV3w3fYtPpU9TueoPwdH3Xmc2gCLEFPnao2+YMS7gJ4G43XP8zim0mfpKAxOHTzb760qo9yr6RWeVvVUYWUtvGb+KpjKj1oLLkFMZW2TNA8mESwX5lnL6rhTxYmOSg9RKOb1t8wFYYeoUndkmwh5IK1KEgkOgxIfrTztZOtpYcHUB5so6V7Y84aK3SILa+Cr0GZfKwxjVNCfvAXVFioelN09LbaD6/JD6jw7JB0r7+oruZku8F0kHwKEgKykWAZBXkBoKDmTvi0sGzuZOSfNEd9qDU+qq4vryOl3ueHH4YS5c2Ocv7f5FvvL2V/nG8lu0i54YMz44fOXOMJzVjRbFiJziHDp01K0JEWOOLHPLZKti8nDE8fEM3YTRpRF7qz260HHr8S3EYW7geHJrmFe1GWgfRnAZP/Z2co9KAl5leFA/i+Qu4UaBjWc22F3ucuvtW+Qu09/qkMr8XVXNHT5Haxx9bUVDpQwsXsjLTIoJVwWyYOvu4RCNMLs7K6xaB6Pi0NYGc5rz3tzo1sxkD3j7/mpa4Uee8WDIbrPDfrXPBlNWsoQMHYklS97xNzleHbI7Pv+HHce/7VJVjpdHvDZ/jVE9IqdIR0e1a5hDPITunQ7Zc7bajFBda8hVtA4gWQPgGyEvElok0tIX+XxBzNfovmbbgLgghN1Amisyt1EjtxmZ2DztK0eMsQjdygpPsdyVbr1aLb4dwGgM04vK4wWk3kEvFgo1yDDAXmQEieWEvOAJu7Z1WPuFslSyBx4nswscWUFxQ1PDaspmQVcIb1I7xJsJrlSCbMPgMw31xyo4yaxej7RvRPKjiD7E3qu6cFr2inN5IbStXdLTLCOhR4Ka7iIIt5Y3eZwOeW7wDFeaq3z+Q5/l6sFVfvPOb/MgP4SxEtRTD2oW78xYPm5tfVxhD5ITZOKpsBVwnzNLt2R8bYLecyxWS1YseUxivDvEHQlh4hnHAbNqRhg6ZEOZ/tlNxm9EZm/OoRF8JbbFGjpc4/F44iziRPBjz8p1fOJPfILBrwx58903EBHSI+tE6Ms4gW2q8lBwtcM3HlfZJsOrJ3fWnak4oosMCJyulvTaMdgZMr08YHhlRH2+wW04klPq08Ds7gkxZVrXGfM2WDEJg4pmVLNbb7NfXSBQ8Sg+5rg9JmUbiaNE7vuH3Fq+y+7o/B92HD90CWeOXrdOb3PYP6IJNdl7qlyxCi16NaP9itRl9FGCocNvOOpLNd19SwkjK1I5qI1diLdtiSYMXyi5Hsb4FWSm5Lky/OwQ2RFkZWsxSlEKT9fIsCz4Wy3hwslo5h1P+BtrIphm1EN3JNTnYbTnWGqxM1wreM/4Htbu+j2PbLqycVgDpWAupILMIYuRiohFvOXgTOrvxdrhpnRYQ4dsemvhfeGeDBzNJxqal2vSQqFWZC60v9fTfWVFXGaYGlWaBLTZGLJzIdfWfaVxIuXMuB4RY8/3Tl/hbn+fi/U+l7cv8ac3/iSv3XuN7/avkOpIPI7Mri+RgRGrxGHSewHxSiXeUvicEjUT3RLOweTOkIODY1b1im63ZbIxZblacXjziHDV4TeNI5FCT/ijFdtXt5i9OSsPu40FTsCLY+/cBW7OblNtBZrcsBq0/PzP/Aw3/+ktumC5MvFBj4+e3JtnBq2QO4erlNRk/MiKRyXB4jdyfuK+3nWsZnOqL9Q0I4+MlXbU07nexhwvdAOPO1exKUO6By2nq9KdFObxTr3NpcFltmSbk3TEUTzkQf+waH0yfY4chyPuyX0+Tj6T5P/7Xh+MwsGa46J87+C7rLQz5+8Sa6Bk0mZCryhtbMmHZv03eKYhLvuzmMPcC2EqhnOU1akUOjnZ9ChlOQ9eySeW0+IvetJpJB0n9G4iX/LgIVyu8KNCDFuqdQgOo5injMZc1q965ghGUPIQHo8rYu3OgEAjhgHZQoX9OaH5SI2/VlmrHbC8lJKLQm+EofioJT3KpMNEjor3Abaw0aoCOtAWMyzqFH0U0fupbGPA16YSlS1P2HIMr1XkZUY2HRt/agPuCIf/wwGL31uYKVFUZKGkmSIjLUQlJfWJNvTAgu1qg9o1HK6OuL96wJvNDZ5prvL0lWtc7i7yvaPX+MGrrxgBroi41uIuY046sihDHTHLpwRfEQWW/ZzhpTFbNyccySkpJwZXB+j3Yfn2inDVIwOHquDFQXC4y45hNaa9v4SBR8b+jKyVq0y9W3F+Y4+QKhI9ly5f5aXnXuTb978LqqRbhaquDordIV0iNYK0oH0mjAIpJurDijAIqM9oUOYPT4makCaTJEGCqg+44FEn5AzOZbIoCzenOVdTHVYktTE8LxJ7bpdt2WLsNxhSc5xO6LQz7o5aoV2mJXfnd8lbivPvz/P2gSgconba5Zx46/QtfOUJOIIz+nPwgVoDeTeRY0XvYbgzQDYEvRvPOA2S1ECy/onFoICNAG7d1QgiybwrWsFfEdK8p38zEd/KEDJhiYFW52yNqJ1aNsk6cqDETJKcbVmKMlYmjnDZ5u2ohX6u6y7CVoWD5yrqDzeEK8Ho75iZi3k12JrVu6KhUaFOlcntZxAfdMRbiXiQSIeZat+KiAQDW0MRUoEn3emJjzNppUhKOAd5oCQc1bmG/vaS5XGkvtyw93/eI36j5/E/OrCRLBfeSmcjYV5m0iLTiwUmOzllo4K9ZoekiTZ2vJtvcr+/z27Y4SM7z7H/0T2++e7vccAxrrGthClZ7C/1QJ8Y6ohVXJGOLTlvtTdnfG3C+NGIjh6tM5uMmZ+ckg4yUkXcRkBEicuOwWSC7mTqsMHpaoZvHF4dSeHh44dsPL/Bs+NnSG2md5G5zHnx2kf49v3vgQh5kYn3I67zFnswsRQ7lxxUYmHXMSNjx8nRCTujbXLtSD6zsb/J0cNTVBNxnpDsSJLPukEXBDGLNDQqs7jAbQieAJ2SouK1ovFDRgxJKfB4foAEMUxEDevyrty8a0Xc+3B9IAoHGIgJjmk1JRCI2hO1o0+JZVoSkxGMqi1bXY2nI7rDnlVeWReQn7yp2q9Bz/V6zdiXWgl+U6ifGdK+3iN1j9+qWH2lQx+BukzY9egqU32kxg2s6utKfwh7MPUrlvTWWXXyex5/1du4kkrbL9nwjwDVlcDoo0P8FU+q1BSfC0zstrYuNFzWJNYFhVfbDSK1EK5WhEsBPVb6Wx3xqMflgBsJUumZ+Mw1iv9Yw0CEdDfT3+yJJ8kiAXrIO0ajzo8TsV3RbXYMPzZk/9o+B//0gO7YWLq5V5hnpHlCu1aBlbQ4BA2ZoR+yU+1QS0WnLSfxhFVasXt5l585/1O8c+cGrx29RXQR5wXnDHgUFVrpuOgvclvuo3FJvBfxe4GlXzHcGprbvMLkk1Oe7UbcvHnTuqVBQqYGwvYhErYqZOwIj6xzS72SX++oz1fsjrfZ9+dZ+BUzmVOLZ3e6g1/a9zrvLTBKgd66Pd84Up8NT6ogpUiYmCHzyfGM7ekmvUv0Vc/WeIPT5QntUYfrEho9KDY2ibdDYb1mSzDdnBLEo70STxKVVkgPD4/u8c3ZtzlsHuJ9hXelWwWCBrbrXetC3qfrg1E4xNiiznk+vvMx/uHDf8QiL2j7JX2Ktn3Itk+v6ppKK9LjxHK5tAc7Y6d+beOJYpJ11izLJEwuTRl/ZkTaS/SLyPIrc2QQ6F9t0WWZjXd8WZs56ivmBJYXPfGRuWTnzsyKaW39Kr2xCv2FYKvcNalDyqoW8FtQfXRAdbVCHfSnXWGxKiwgz5S8NE0MJZENAVdhJ+DAMBtXO9QXTU0S3GaAyoyF0omSRx4/sVUzCK5SZATVS4HJcyNWX19xer9FjnrSA4fbcIRNb74TJ5FFXOC3PeM/N4IvLukfRWQVYVWRlyCNRTO4JpGisJSOpBaGHTUx8AOcOiq1B+xR+wgRT3NxyP7gHPeO7yNOcGUr5LDPaqFL9v0uD/cfllVtJNeZOEyEI8ficEmz17D9R7d4+tLT3L5+x1r9AfhxoGo8ta+gE/xGQLvM4iunhM2K3avbDPwABE7SESkoXhqaOiMjh2uVPtlnoNnGXZcgjxRpPLmPuKEjV1LUrELWxGw5Z9JM6F1ka7xJdD0shKODU8I0w47ipw4mgAqh8ngP9WZNrpSoEVd5Jox4cP8Rb67e4rg6IteZEAKVM8tEyYLLjoGOeLZ+9n0No/5AFA4tTSyifHr/M3zszsf5nce/TUpm2BN7W5eoQr/sWeSlYQxqLFE9UfJhpnq2Mep4a7Rhclm5etAtyLuZ6DOr11vyAoRo9HCvMALZskS4sO2gcYait4oerMOb81m3QcrIWPAXKlxV1qdnKxsznWk+NSI849E+W77rLJHnip5m8iyTF7alMQm+gaZaYh5MYxptDBkIbuzMJqAunVTxCT0rnA8jURS36fC7Dr/ryVse3ydW05bhz4zYuLfByevHzI6XZI+Bf0Nvf6aD+DiTJzD8+RHy9SXxcbQC12ZkJbiVok0mklGJRtlPmaSZLrcEV+Ew+nqnZrjU5o55tSrbBFdk5bZ69zhOOOUqF5hUY9wVQaLnUXtA9pHx3pjusOd4cUo1rtl8YYNn957i4cNHrCYdg7pmu9mk1Z5YReqNmuUbCwiOyaenVLVnzIgdt8u32u/gvOc4HcJKkGmifnZAfGdOXilOMUc5tblWckY6g6hdFtJRLpwf42GsuhVbbpPTyZxxGFE9V3H6z05pU0fcToRtIexWhD1Fd4BBMAypSzYGlaygB/4BT124wh+ZfI5vPf4Oh+6AQWiYhjGVVkzDlPGDKc+c+xDv20qFD0jhOJMNq7I12uE/u/i/49vf/BZ6RVj6JX1vXslpZT6ZFn9ogKEeZVOrhpKR0hkzUBNmE7dbIU6JTcf8XiZsePobS1MuxmSJY17w2waIaqNUz9aFfGTRiOlxJJeiQQsaLRXMX/Am5aaQyLL1OmEj0Hx2iNtRWFlXkB8n4lFGZ2r2g8tcRiDsz2y1aFgoCtpUVL/lPVoTjEYOt2OJZIQy+4pZBOQjpX+1K07hgmw5/Nj+vtrsTET2oYrqoCLfjaRDwCthP+CmxkTLCyNZhecCWZNZCLRWoNNCLMlMUmFXluVSzixkSRBf+Ar2K+VMm3v6GI3LoQLYanxN6XYCB+mILb/BUT6m9hUDN2DJktWoZZLGnOgpR/0x42bM3oU9dra3OeiOqMcVF/15bqze5bSb0z2IdDdbxp+ZUI0cqGcaNuhWHfcXDxn4hnerG0jvkUuBaiNQ7Vf0bxeqfLbMHnrrSKSxmA7Fkw8ijG0rhghpoCzzivPtLktpWQ1WXPn8JW7+2m36PqKdI68y8WiFH3e0Q4/zNqJpl3Ejz1MfusrPP/PTXKwvU0lNu4p8Sb9IcI6AZ1JNEDzbzR5BDdd5v4rHB6JwnLVgQuksEl3Xc3LvmDZ1aJdt4xCN6q1Ls/zLy4IPZPAXTXCVk2kA3NjIW/XFppjM5CeAafDgetvh14JMrGDIHKqrFTIStEvmx9Eq6VGy1Pel5cn6DRtPCBiIWfAVFU81hdFPTUi+I51APs6ko4weJMt6nZss31LkSyEqdGSEJ6NQ+blM9FYCoQo3Mt1OyNA6C9kSC4AORUxWW1K6zkDbTOzUNC+N8SncrqO6FhhfnTJ7d0a8m60S9hB2A2HLE497I4yJoH2ywOQ2o7XY2CIUda8n17Y5wAm9JBzF8EiVpEqKmdRGK4bFTFhKcREEp9C5jkoHiNhGYRKGLOOKTLJCuXCkmJinGQNXsTnY5OrwEpqUWhoqaibHYy48HnHzxXsMdgPjMGIrbOI18O2T77FMS/q25S3/NukkFV8RR321YfFbS6Nzp4RGR46C9GobmhK9mZyZXkvpZF2Etm457Rbs1Jsc3DlmOTji0p+4xP0f3KOPify4J9/3pCoh9Vr67/Abnmo/MN2foKL09Dgcu+NtwszhMT+SShqGecTn+DRN1fCHsvp//Vr7hIqw6Ob84/v/hPCio32zJZ4mshY8vs/mLxrliftWsX4IF4O18oBoxm0GBk8NGUwahvUA7zyLbsXs9owcM1LbDaEepHGGZ8wTo5drxEGalfGkhfTIuho9zbhNh7sQbN0pcNYSZMFvwOSPbRLpSKdCPojkx4l0okYNXxlFPM/zmSpXRsbvyIUbIqmsZdf6OXnP38WXrgOkKbT6h5DqiNsQNAiy7YzMVStUShh6Q/CvJ/rXIv6ckB9l0tUTRs+OWLw7J94xdm13s8fXHrfvcQ10Rz35QaKqvXljdoqulLxG+DUbnONL+7EGhMv7ogp5aZ6bvvKoyJm7AWrFMrhAyonDdMg5Oc9NbjP0nmFqWOWW6DJ1Cixdx6pbQa20acWpRuYP5tw7fshof8gn9z7B3f4uWxfGTJsJm9UmO24POuXGydtUk8CwGeJ64daje8g5K1yVD7aqVxv9RNU4Pj24aMpb0WxFriur/U6LVWDFsT9mnMfs7+zy5p0byEW4+qeuUjUVi5MFR989YvH2sqzqgYEpi2UIp+mUWZ5zXoWKioE0XGwusV+dY+SGTPMmL81f5nPTLxgwug4ceh+uD0ThUBHabknMiZQiKfV24nS2bXBRn0QhnOXH2qyMA7fh8Fvm4+nqYKj9QBhMGrabLTbqKUkTfRdJxz3SOPxmMHl8b0Bqepypzgf8joX4xOOIRCE9isRDG0PknMPvBVwtZ+pX1E5XVwvTn9tAx4n8OKOHNp7kk1KAllgHs7I1HRXG3iz2hBLlzNNjHa4tHjSAcx5poPpwoH6hhi1z46IyAls+ypDsZu8fRqPA9yBjC0lyE4f/rEfejPTf7sm71n2ttlZMnh9z+t0ZKXe4rZr2lSXubiBcDYRznu6xpdCzKlwMl8+8RDSXDVZVHK+EJ9oMEev+VsaulVpsc5ZLffGOQEAzBAm0uqT2+6SZMhvMmVYTkmZqV7O3cYEHx4851VMe14c4HC2RUJkz12Y35Zvf/SYHm0dcvXaRvfoc236LKje8fe8Gx/GYkZ+wX+/x4K3HzBdLKlfhgP5IyatkI4STM38VmqJzypClWBfOM86JWSMUbo4GuO/u8/zeh9h9tMvBowMOjo/49LVPsPHCBotnFrz562/xzvV3jY06CEaPHwiV1AzdEC+mQZnUU14OLzGWCaM85IXTF/jM9HMMw4gCDf0h5fxfvw4Xh/ztb/13/IUXfplz9T4qGTfAaODqcQjDzSG1VOaipCC1t/XjNlTTBrcU8mFi9miGviw0oWEcxjSuYZmXaMj4vQp3pyMlKaCkIz+0B7r580OjHD9OSDI2ZvudiHQJRiXQeCRl7WkjEb3pIgYvNbiLju5xNN7DaSLPM3mlVjhK4r00RmE2z1EtBsuFd1JYpeIUHbizzBK/JzSfrXE7ZpIjrQVTsaSAqhlmmSSO5sWK7k4i34nocSaPTX8jwVF9rMJd9LS/uSK9menGPdUVx/DakPkbS0R6cI74Zo8eJ9yViur5Gl1gmSr7Hq9Czg5HskmqK4zQtdpYCzNWKHiTnZISjJRHWbtbBosRnGoqkiaOOOaZ+BTfufMdOC/sDLfxXrg0vMzV0TVuHd3ixvJdfBVwCMf3Zuw0O8xOltw9fUBzsaGh4bzs4bLnzqP7vH7wBuFcxblml82wxf3HD8El/KAGB31qcZue9BBbqSRXDKfBdZmc12O0Q0Y2OuWsyNp1royS7/ibPPPsVWbfP2Xllty7c5+teptxGDGdTG1lvO0Iu55mv2Lr3DZXB1fYYIOBNNTULOOSSZiYV+095VPnP8uwGp09I++fNvYDUjgEZXu4xY352/yVf/BXWB7M4ZIwHI5gsGIyGtHsN7STHkVp+gq/8qSFEuc9p6+uiO8eEZc9fU6oy+xc2aG+VFGXv7IvcvilcSsQcFNHOsrk+4nhTzf4iSMdJwslEoivRtK7xuLyOwEZYadrUEPYwWjgQ0f1TE3uolkRztVSxgqrU/tyzFY2H5PKiVVGLfo1AIk9fFu+5B0p/rKn+nBt8pYHEZ1ny03pMnklsLKixyKb+9UfHVA960ibgfhaR7oPcmIbEUkQLjr8XxjT/qsl8QeJ1X7P6JMV/p4j3i/EuEOzRXSrHn/OkuvyvUSqnBUAr7ZtllysBniyUUq6nhctJa4HX/Q16Jr+ZZTwqJngPE48Xjxdv+LS9CLjN8Y8fvSYzRemLIaRN/RNtsM2+zvnWZy2PMwPSBn6Xjl2J3TvdkhlLNmRHzHxGxycHvL6O6/TTSOXNvb50OhZ6vs1j298FfeMkbLiUYItYeOXNzj+28fkFqwMqAnbkpEKJQu5VmSu5IMewdN1SrXvcBM7CDrX8njjiGvDy7z6vTe5deEO55sLHC0Pebe6zfBTQ/zQ0UwbtqfbXBtc5lp1jY2wSU1F6IRvPP4GjVRcP77BRr/JXzz3lxjp6H1dw66vD0ThQIWmavj5D/0Cv3b8q5wezuh/u2eyM+TSJy+j2z3H+ZSu78nHiePbPf3NHn1grEbpBa0tkWzNouxu9vARpZEG7yoGAjuDbRZ7C9obK1yhpfdHET92hI9UpIV1CZog3o+0X1nZaLFjRisMpBgAFXl9UrI4/NhZHktrIGqaZ1vj9mojxpUGNsSYrUA+yvRvRutWSiERtcIiWw63b7mqlhsD/Ssd+TChpxi2o1hquwcQ47QLkGH1zZbB9sAYik/X6Bs9+TCRj8RS22cBvw+DPz1i+T8t6b/d013oGLzYMP/ykjS3eM3cU8hsGb9T4ba8bYhmilNPKiOk2TqqrWFVTa1b8A4tmIALoRD8rNmunCeIQ8ThcaQcLYDZ15y4Iz721Ev85re+yO3FHT7y8Re5PrvBYmNJbHouTy8S5x2P/SH12NPe6lgdr/AXPWEQWMQlrz5+g7dfuc7paMHT56/yqc1PsN9e5O9/7b+nqzuajQbpoX21Y3CtIWxVLC4v6e+2lsXTrIOssM94kYwgeJjJ80z/ZsKfC9RP99TP1lRT60QPNx5z6eVLnLu+y73v3eNL73yJj33yJX7hoz+HbiYW1ZIgju2wzZbbYtNvUomnyQ1ffPBFvrd8hRRbYqUs0oI789tsN1s/lkfuRwmdHgC/hdnUBOB/UNX/UkR2gL8HPA3cAP6yqh6W7/kvgL+KnYn/uar+yo/l1ZdLRRHx/Nyln+XZV5/l+7s/wI0d8YHy8M2H5P3E89MPcf3tG8wezsin5YSuWFPrcMNiiFuqc/vuCj+rmU4n1H5AJNLXkdur21TnPelY6N+N6EGi/vkhNIIeJliCk0z7RiIdWDfk9jw0YsBkbSMSqfTjKOF8MDwiKnlVKOhkqksVMpUzbxCWagrRSqieMbBQE0gPtIm8sv+H23em9ETIC+hf71H1MEwFC4G1+Y+4wlORwnCdK92vdwz+ZIOMFDlvvhT5BOKtRL6fiJdrqqcTgz/esPwnS/o3e9zHoXo60L/To1ue9Eay9lwEXKS6UOHPO6g9ehRh4Io9QcY1tnp1uWg9sNenvcVShiYARqDy3lLaBXOoTy6RVHB4BMc8Lzl/4TyXJhd559Et7r5yj3MX9rg5v0W7u+R0tGCz2WC1XHFKpn1wCiMhbDlGozE8glvX3+XcxX1+6ZmP88LoedJc+Ydv/o8cPHXIoG2QAN2rkfadntGLQ6qJY/jhBt01EFRXat6w688yi42CQ7HEuNqhDyLLm5HVt1aEvYpq38Pn4PHzjzj3S3u0X18y75d89/j7vP31d3juyof4yDMvsLe5R+0bgveA47Sb84P7r/A7y9+lDZ0xTp2walpudrf4qLz0Pg4oT64fpeNogV9U1ZmIVMAXReSfAX8R+FVV/Zsi8jeAvwH8dRH5KPCfAi9hafX/SkReeP+Dp4utnK73/rAz3OWz4bP83qNv2wZiGelWHc2FmrceXWfZGZHIo2iNRRU4836QkRQ9ijE4c848+PY9Lu7ts+kCM064cXCDNkX8nie30H+3x+06wocDulRoLfCpfyfRv9LZlmPT4TYMqDzrNEoqu7Xl5nNJzmiXiwIX3EZAK0WPbQTRtpDHSniTTDHr/IeJ+Dijj+yh12RqUvYEf83jt4XquYr0dk9eeJOA94YVSPG8UBWY2fcDxPuJ/kYkPO1xQyFNPJIyLjp0kdHXOvo7Hn1JaT47oH+1JVz0+HOe/hZUVx1kSLcTPpgtXzpMyEQIe0JeCek04Xp3FgBtgVUO6fTJZ+shbAXUK8F76hAY+NpIawUbyErJYQEtW5oHPOS5F5/lzr+4y7vXb3E5XgIy3Uni4eZDHi2V6eYW+4dj9q/swwjq3ZqLeZ893ePKF64wrqecrI74nVu/yzcX3+K0OmGyPSbMAo9fe8zsy3PqaxVaKZNqg8W0Y7VYkY8LiK2KjAW365DKF99UNWB92+OmTeHiZFzliEeR2VdmVH3Nxss9z/6RZ7hxdJMUIqlJvOFe553777Jza5sLzXku7F/gYHXEnfYmh5yiPltERxGxZYm8M3/nyTpepNg7/oR4HGo86HUkVFV+KfDLwM+X3//bwG8Af738/t9V1RZ4W0TeBD4P/O778orPXtga6ilvCvDw8QP+1d//VdhQUh/NjSomhg89+WLGqaBzj87K2i+rGdXG0gk0BXAbGqFrsbHi/r37xEuJdxdvc3jvCLcbyCee7stzcqsM/9jATut5Jr8bad+KxLd7dFH29WNLg6O2/wfFNcx8OLCvGTq0LxsSQz5No7LirAtYp7utyV56B3RlPiLaKVKCnDQpuUvoiZBuJNwFR7gWkHMe9yiiCynO7Yr6AuTNrGBJMBap2xHyUUZbS6uXUbYTdACa7HWlg0z+cqL6lBCeq4h3kiXOTRy6UuqXKvK5QLzdGZaysC1RajBv1vugq4SOHS4qqEUB4HOJUHCEaTD/DAHnBIcja1l5iuEcgUDAQN/WtQx1QJ96xucmPHXpGq+99jq3X73N6MqQ+eEpWxc2Obe5z+KVOTFBXPTUxwPc0HMynPF445Bv3f8OR9MjFoMZqcpQCb4J7Ix2OP69Y+a/NUdqoXq2YbAxYCduc/P0Dukk4ZzHfyhYB7vOA062WlYVUCHNrZPyFypEzAslrMy/5OStE/wxbL+0y4Wdfe7rQ+syRYkSeVA95GE64NXHr53ZEJ5dUsYjlIjwxup1utgZhwPeV6zjR8I4xCR63wCeA/5fqvoVEdlX1bsAqnpXRM6XL78MfPk9336r/N6P5VInpJS5dXyDX3/wG+TnMnLiEY3kpSARljcW7H7oHOxn5scL+rsd8ZG+x+fCaNn+ckW1Hwg7ARkI8aTnrW++zZ0Hd0jDiAwCIsLqSyva65HqQwG/H4xQtkhogHQnogeUACOxlaY3XQWNZYSos5txXRDyIuMa6yroQPuyknTYiRELSLp6T+FYC+SiyfXJcuZ3qophHyLow0w/6/DnHW6vQmuTZNta0Ixn6G29qV5xjeCuBUiZfDPhr5nXqq6Ln1E9i92h0H+9Z/gfVahzpEcm7EqnCWkgPO/x5wd0b1r3lReKHGajv48EPQQ0kQcmRZfaPDEYOaodj5/aGWVyAqHPtokJ4gi2syWqeY1EehqtCS6Qc2LmZlx5+RJv/+BtVsuO0+tz/J7j8fUD2u0VH332I1zbfpr7d+/xnX/+PY6Wr8FlITSeynl8U+EqbzJ+n9mrd3Ff89z+p7fJTqkueZrLFeNmzOo7K1YPZlRPNVQXAnlR1uhoCUISBsOawYUKdh2RRP+4I97pUQd+YKmB1b7HDzyzxYL59+YMd0c0Tw/ofSz6qdKxzpU8MEd0WfskY/cKhaToGs+t6hZLXVJr/b4DpD9S4ShjxidFZAv4hyLy8u/z5f+2V/hvME9E5K8Bfw3g6tWrP8rLeO8LKhzIzJ3DO/yD6/8D/+Sdf8KNh++yGqzQebL2cJTROcQuUT8MXLlyhYPxAfdHD1iNlqQHCWaWc+I2HaMPD6h2TUwWTyPxSEmnHbPrPW7LM/50oP9aZPHNBW4DwvMV4m07oXNHfCuSH5kSVLRIozecbTp8MdDx5a2IZTRpIS0TMjHFo3bZrADHHqlMbSmBteatFCTswVWsbZcfemvKu23fZz2iMz2O65CJNy8RFZO+i1oGbgKJ4M45XJOht/bZ7TVIKNuPdUBUoFDkrditvhKZ/idj2lutjV1q/AxXMm/rqqK/Hq2rWpqPid/3dPc68ykJ2bCZiUPOO+qdCjcywVnGBHsxJTO3EUGcJ+aEakIUjg9OmeyM8d4TNeOc0qYl1V7FpYsXeeu7b4M4YmfcmpMHJ3z17td5Zf81nn7+Gp/5P3yCdtGxmM04djPacceKJX3u8Q62T8/BN5Xv/avv0ucedz5Qfaim3qkJDwKvfelN3OWA33SkIzO3poOmrpnsT5k+M0UvJlZ1T0o9vvW4nUB7fUV/o4eh4jcttoFgqX9uy9HGnrqvaUYNXbeyVXuE7m6Ha6C6MjgDtSmrafMlBURZVAvm3ZyteuvMr+YnNqq891LVIxH5DeA/Au6LyMXSbVwEHpQvuwW8txJcAe78W/6svwX8LYBPf/rTPzKlbZ2tEVPPr13/Vf7b7/8/efXwNbq2NwNZ5xmMGpZpSZ46dJhJh8rs7RmbL2+x2+yg25kH1SO6aYefJ/KpgVgy8vZMtpm0Mmm4DAVVT3WuIr2ROPnVE2tdr3r8lrOV6SIT3+5pv9XZ5mTgLCNFSizBwOb8M8PZjFnN9aA9pIOI2xRorV2lOGm5qSCbHrpM7pKd9t7AYDPnLkpe/ddOHdUnbezIVLtu6pAaNCWoHG6Z0ZFlpNKCRIEq43bttWoCcY54L1JdDqiPZCdopdCVn8MrBCE9jKQ7mfqphnTQ20torZtzlcNfDGghw2k0P1JfC9UFz+r1Fhl6QuMKpdqG9HTS4xqHx5O98WKolCzCKrcmchMTvbmho9eI5kySDkcNIrS64uKnLnDjazfp2x5ZOHRuhjlSJQ7GBxy9dkz9VMXwhSF7V3a5unmZK9VVmtjwtcNvcuPNt3n7715nddia3+uWUD3rGX9oRLhd8favvE1Lh3cOPU6EULF5dYM0Skw/vEmsepaytFS3mMidMX/zwhS6kjFGbZvJC29F1JtRlAtCVhjXNZDpFn3ZWGUr/Pc76nN1GYv0jKq/9vCIRNrYnT0z72e4yo+yVTkH9KVoDIFfAv7vwD8B/vfA3yx//8flW/4J8P8Vkf8HBo4+D3z1fXvFYkj8r731L/mvvvN/4/7iIeoSvhJyUHwINE8NqKhJmsw+7UHP4p0l9771Lh/6wouM3Zi6OULOl8O7S/R3It3NDn/OG6HqGFwWtDL5uGQ4/udz6yJ2BX/RzGhpMc7BEUW+Lmht76wLYua33v5d3pNGpsWXI8dMfyfiz1fEhxGH7fw1ZagrXFVm5OiMSZkploNqIOsaG37vPSH2YLstYfALY/xlgS6RS7ZH99aSdOqQU8V3oKFsAoKzgOhoNgVrF/Q4z0iDyfIpP4/jSchTL6x+b8n42oi0MDVwToLfxSjXTgm7zrRCnSK9aYTC8zXhrqWSaTLeRl5mYijdWbbwJdeZ56prMrnKhSfhCLW5dbkgLNvWgMowRtQKTkwd7pJn/+lzvPu127iQ7NSuxF67gh5k2tMV8WbHYn/Oo+cecffiXS4PrjI4DnR3WlYLc5GWgSdc9TQfGhBfidz9zh0D1DdsU1XXFU9/8inmTy1IJFYyN/A5mSFSd9gRH0cbUaPR9N2GI5+ArswdXhJITsiG+Y7kWaTf6CAIVVOZO7+aRkdXSne/I2wF/Nizju5Y20Pm3gyS112Gvo/c0R+l47gI/O2Cczjg76vqPxWR3wX+voj8VeBd4C8BqOr3ReTvAz/AbvH/4/u6URG4cfQ2/83r/y2P9RHN1IFUCEKfOtJpZnXaIiNMtq2CPxcYDj234kM2Frv0leUbDJrGgMKh0g975t9e0L2zxI+CZWbsBuqtClph9i/n5HmCzYzfrHFjbyeXYn/Gw2QjRRD8poeJ4ryDDbG9vlN78Ao/IS9Mv8JSSdcj/YVobfvUmU/HEvK9SLhUES571PXWYWi2MONeDSspma5PdAh2c0iA6gsD2Ix0ryvxzd7GsV8cUT3ToG911qUs7S4wRy3bKsmGwKkza8OcyHfAX/KIi+g6d8TpmXlw9kaJj7MMM0h3EjIX/IXS6YwEnSo+BXgYyVlwfYZQ0bzUsPjyClULa05VobvXoCuzZsyVgaV4zKAIuxNj4/BD61Sk8nS5Z6YzWrdiWA0ZyoDOR879wh4Pbz02S70KM2ZeA9WNPPGZRZi/veDtm+9yo34XKgvZ8mPjnfg9h79a073eMr9rn4cM7fWMB2Muff4i6VKibyPdoiOtrJuNx5F0lEinxWtWbG1OBW7g0HvlPckWwiRSMLEBrO6vcANPmAaqeaA77fDbzjqwZJ6w6SRBD35qhXSNr/noTEvDGiv4CXYcqvod4FP/lt9/DPzxf8f3/NfAf/3v/er+LVfuM//yzr/gIXfZGm5Su5omNPQamcUZJ3FmIbzJTqa8MEPZdJgIWy3f899ns5kw3ZsgHipfWYtXQ/f5jpNHM6JL5IGSZ5F0qqx+a0F8ZOtENypiuGLNJl5ov90TT5OBjh7770GMHzIwTGGt6NSYTZW7sHlfV6ajiTd6wgVjVlpaWSYdmy1dOF8RrgRyiKaB8ALRTnWyPeA2166ZIYaR+PNCepzp3+iMh3EnEq50hOcr/MSTnAVBMwOXDflPj5T6isdtQLytoBW6iKTHEbflLQ9X1riKdT32j4rOzCJQM3CcLM1u1wBC1wh5LLjem8ZHBE2R+oWG9gcd+dC8LFLGwOJB6dwacyWjsv/v2emZi+uWls5uIxDGQr9XESYVfYqEpmKv3iI/Jzzzi0/x9v13zXOzUNypyudUjsS1cTPetm4uCPWgot9b4TYD7mogP7D3kpLVqlnY2tji3M9us9pt0Rs9j7/0mK4zF3nJmPXhxFFNg4HjtiN9Ak+MjXFMMsB5LeCTbSElWNxbEh4XWf3U4cQMkF3lkL4QBpdKjpEw8aimMzbtoBqsH8r/dRv5PFw85LXDH3C+Om9CJ/VUrqLNLc7DihWL0NtDcJhJDyJ6ag9oO+vpHx6zGi3ZnE25+qFLuLoQipzjyB/TXujIixX5uCceKf23lvS3e8uFHYKM5awwSOWJ3+3ovtnhEnYKV5axQRGb5cNkpjhJobP2Px+pZcvGwhsJgp5m5KkAzoBKXQOgLfSvd4hX3NOeaupIu5C2eriTDYzNcgZIUgqaLc2LYG4p6GmE5EgnSrUen3rTsURfNkCiyDDT/SDSfLyifrGme62zyMhWYeqQabaAIgEtVoCIliwa+0c3FXQO6X4m7CuMBa0EVytsefReIifFB0FGQnU5sLi7wiWHlBS7PDA/CwkARuHX8qSJmhpVo22ENOYSV+FxIRN2K6rNivySMn15zLSecu3zlzm5fsKCpZ3IjieAteNspU/Z1GsQqlHNtt+mfaGj7yK5T2h2xtDFcJydazts/fyUbicyfFDzzj+6R6st9eWacKmh2vOEzWCcjgr7gXLmDHbIwEuOfC/Rv9nTPlyRj5WYFE0JP/CkPuG2HW67MI+dUdnzkY02VIoLa39XxdUeRRjJmEE1OMsd+onyOP5Dux6c3uVRfIS6RMoJ7+yMlRJv570z5D4av6G+MsCPPdolugeR/p2W9qDlUR/pZ5HJyxv4xhiJq7hksVzSHUVoFbewOMKwb6Y0rvL4qcNPzVCl+/Kc7psGypGxD7XGZngFop34brOYB2cxT41VOSqDWn5oI8jU8JC8VoAWZy6NRvpqv9cx3Bohm4qvHH6zQa8k4p1EfDOS7phpECUJwW04VBz51MyLiA5/RRCxmy33GX2g6E7AbyfSEvM5DYIsE+3XMs1nBtQfDrTfz8hpJB8l3I5HjjLagb1QUHH4PWfjm0aoFVpnHchRwu1Y16CNScJ1iY19Wa2A7FXIYkGqQLrSqc1SwVAKqFcsEQvzyzgRueA/DsKlyuwM7kM67uikZ/bVOavPrXjuTz1L2kpceOocd9oHJgyEs+0DrB3YCoakMBjWnJ+cY3RzwM32XWJnpk/M7XvSCrYvTxn9zIB+p2d6NOX2P7tLvJIZPTUibFd2rzTubNORVXDlPVuTAdfjjt/1DF8YwvEG/Swyf3eOFoxOOkeaReP/RGeH0qnS3Yg02x5/OcBAzenNO4ITpHc8Lc8wdEOAMwLY+3X9gSscL+6/xIffeZFvPfguZGXaTBiFITknltrSh2L22xrGoETbpQ+F6kqNrx3LG3PyaeLo+BjfCd1Wwm05YorEPqNHyUab8xWjT49YfHcFj425mY8UPYjEt1fE2+mJCY+TErhU2JhOkeTgHGc5sLS5RD9qWdk61JtYzg3FaOtzChhaeBrF+CXPYfXFJYM/MsSdt5WoBKEaBNwWpPue/DiTbkTSvYy/FmDFmd8IU9uuxEfK8rdXpIfmdE7KVJ9pSD4X9qYaoa1T2i8vGfziiOppJb5hJjwsErLl0Zk5IwuF4Xkt4MbO7AqCs+KhSnqYCBc8MshnJ7wbQ36gVtQ2MzQZ5xPMIQdAIBbN0FknwJMJ3VAce50ygOZjA6qLDnohbnviOz15pqTTzP3vPGTVtjz181fxm4GXNj/MqZ8R6eljT0vHSjtSTma7qBBCxaQZs593eP3163TzhD5KBmAmQeaZwVZF83Mj2IXxcsr9377PLCxwoqS54EZG8tI+Fr8RwTlH8rb9EPdE3SvOPAUSCZko3cPeDqDgcVUwXMaVSI0Vhp1NHKOXB9TnBoxiw4NffUizP4TNTLuV2d85x//23H9MXTVPLFn+1zyqeOepZECKLeI8p/0Jy7Qg+EDUSMy93ZBLi0WkBAhb4G5GLjpGW2Piifl3HqxO4Q6EzhvjsnyQsi10tzvEeUYfG7L48oJ4bLZwVCDnhWpckW4m8mnxNF3YKldLBoiiuJcCuQcpMQH0hdLky+lWKbJr5r84O7BN9arFApDSgSjxgTL/lRnVh0zSrae2ytWHQnaJ+mmH/2TA3VHcOSlZLQoDh9vCxpc20/8goysL3u5nimxHAKTCXmOyzUrulfa3lgx/aUh+aII1nWNpcnU+Sy+TQVn5TopdAJiILhQz5RNFNuRs9HBbHu5H8sJWszliYVUPI7kBxSONubtL8S2xvvLJpUGorgaaz9T43YALjhTBzVvysYkMtVXypuf0YM5bv/YOL3z+Wa7uXmVjvMGKllVeMU8zDtMRLR2pGCdXBLx4Tm8suXP9PvlBMtFhCyytcO59dp/R5SF6Cvf+xR0O3z0pgAW4B5F0NxrwXIMbOdymx0+9dZcjX2wE7P3woQRjOUFFCJdqwjkgmdesDwUAFmdgvPd2EBZmbbvsmNRj/svn/yvysOV7J6/ymemn+MTeJ9/XhPr3Xn/gCocTx2cufpZ/8PAf0OqS7IrZbb+g63v6WSKvMn6jMlCyKpwDspnaADqCqvHkqa0jc1fAKOdgaUFJKLhzgmQlHkQGH2tYfG1BWiiyNI6Gq8E/H/B9ID+MJifPgqxjCmpBJlLwjmKyg+C8nkUZSCOEHYdsuQKKSQlggpyLY1Tx3UBsXZrmybxATiEf2yysx0q3UPwLQrgouC0xfKO39XG4VBEPIu58QMikhybbFzVegdv1ZhG4xFSppWilB8rqay31R2tyuyJ3YuKt2t4rvFjBqJ39Cg5SsgejFkt0O1b8uQwjZz/zwP5b7ik2Apk0qYjeMCA6NU2ME1wAXYOY5ZT2FxzDTw2oPlwjo/L5RkFmPf31nv6VFoY28kj5Obq25bUvvckoT/jcFz7NlcElsipt3XGcjlnkBb10zOOC0zyDU+U7v/l9upudua91Ch78lo1l0ysbbC8mfPfvvcrpG6emRRqC2/WEixX993uLiKzLax8Kfhrw21ih23BWUIbOuDahbHi8wwVLFBTvqScV3WFv/+6KzEKM46EOk1YEz+iPjHj6qWf4yLkX+VMlzgMKrvFjeA7/wBUOAT67/zlenrzMV0+/gs/Qk+hTol8o6dCyUv3Y/BDwYgUBPZtn13kTUgsuBFzIFpjssACdAp7RYlnk6sgV1E/VdHcSehztZgpqN2gj+Kdq3LlEvBlLIbL0ehmUWbwS3KDoMRTrGDz4QUHKNzx6nM5GEy2dxnpzANjpNfXlZtAnJ3JvGwJdKOntSNhpbPOR7WanE9RZroweRORiRb0vyKbR5TWZAXI+sq5pHWGZizdm92pP2PT4jQpd9uhpAVOdPbSy7QyvUUWxrYSMbH7XNpNOevyydD1io43hORlW0N/u7OR9JhBvpvJw2MOEmAAxnA+ES4HqSkV1LRhuVCIhc5csp+ZWT/+d/sl7lp39TF2iJ7LqWr787a9yen3GL/2lP0YzaYipxxdMyaVA1kxc9tz+Z3d5+K0DcyBzihvaark+F7j04UvU08D3/8fXmD88xV90+E2H23KEcxVuLHTfbs9GEopilVWmvw3pQYff8LidQiLcUtzA1r6uVrLLSA2+cbiJZ5gCXeyRyuIhXLDgLVI2kqFk+mHk1uomH5WPGLv27IH5cZSNP4CFQ0WYDjf4S0/9Jb7z/e/QuqUBiAp5mexDGpcs1cp8FvWMh12eQHkySqjm8lvGwnS1J62iTe8VT2INj5TqSkXOmVQArzwz/wzpCjV84KierYn3OvKszOJiLbtKsb/rygjhTC8itYGiTpQeO4EpAUv/euFwY5ChcSbAHkAqO5Gp7GHX40IuarPxTIaQ34z4iw0ySuRWyG/3sOMI+0ruhXQ7o0f5THinxY9VMkaYytB+tWP4J4aksRkNmbepFatqK9h2otRmN7LxJRdDHzoxzkouRSUIbtOj80RaRfTU1iVuJFRDyM4jYwoYaJ4k4ZKn+Whl3h7DItDoLfRJl5BudMQvrkgnZVRcGwJF0wQhRsGPGvnOb3yXrm35o//JF2CixNyz1I5lXrB61HHzH9/m+pduQDYeiQxtlBxfHXH5Zy8iA8f1f3md5WJFfbVi/NSEydURBKHrW5aHLYNPDuneakmPy+q1spWsr8QwtFMlY0VbM4RNQZzxc3xweGejWu4T1UaNPy2F0hvvZG1oJIVBnKrIve4OrLU9P+brD1zhAHCq/PTVn+WzNz/Hbxz/Gjkrqc1o1JJKZieIaaPei8gXLoWaS1NJBiibjNImw9lq66zOlFVnXib8tKB3Odk+MpbNx6JI4kvAEo+SiZEW2WZ6Zx8ysdzUWhgX3jqTvCwtLcIPN5dadAg29ohloJVtA4WRClrZn6vJFUo7ho0EE6h13+yoPxkI+wE9r+CV+HYk3U7k06JVKXZ2puKkPHgKSUhtIt6N+E0hHzhQc4qXDG7PCiMoKtlGkdJpWcesTwBfZ+OKn3gLAE+FhxJMWVtfg/ZUjfQVBJFSpA8S6b79YDr29uemTF5But6Rv7Fg9VjBW17w2o6QQKH7c8bZyEF5/ZU3yN/KfOwjL5l0/a5y61u3uP6Ndzh+cFq+z1meyxAGlyqmvzjlUE45/c1DkmQ2npvQNANW2nJ44xgZOvRUWX1/wfBzY6qrFauvL+nvRdZhz24oyHZNfpxMVl9Ig9kcjw1gHooJG/tMdpBcLI5x5XN34H0gR2OR4oHacbqalahx/2MvHX8gCwcI02rMX376L/P173ydYz0xw1hfZm1vD7qW1Sau7MvXEFvOZ+n2mihp8TYPauTMMyPn4o7uSufbF45CK7BVWH7LMopEgV5IM5A64Tc82WfyfcWft1NBnQm5sgBr+zwUGXrok8n+PWRn3ZDRup/8co29LrwWopKePRhSOaOpZyU/Utz5Ar4iuKmQ3o20X1TcXsRtiZ3KUW3MEkgdMANdYevQIMiOAyfko4ieQHylp/njDRISWoKzZUuQia2nc2fvtRs6ZCKkWwULCeU2TjbOSBWQscKJjT2q4AbAQIhOqI4Sq5ngdsRyXYDmODH4ViTt18T9QA5YCt3NSH6zJbdCKl6uUsygxQvqs70/hbBFZZRsfzXwYHyfX/3eAf3vLFm+2dH3vZ3qDWeFRiqh3gls/swWadgx++aScClw7soe6TstD958BN4Oh/rigO6tjv52pvlopjlfMf7pKbMvHpNP7fZTwFVK/VRFPMjoccnHCRkNztjOXs2LxUOuMNwsFLwiOMSBDxY+nlEzo66Ex8tHhavx438C/8AVjrPGAeELl77AT934Kf7nB/+TtdS1K4QeOUPiS3qPcRTATtT1G1v8MNbmPYBJxgcOGXgTgkU7SUUhnaw7Doc0mTD1JM1IdoZJVOCStec5Wk5JXiZcqG1bk0G9FQR1djbQlpNm25EP8tk87IKQXJFNi32fFC8PWefErovKuniYURb5cUT72kYDQBuQsUePMumdTL4jMOKMobiOvpQdh9sJMCyGQT2QFF8F8tCMk40K7azQDQX/dADJZDxylJChN1+OLYeEaMzSYaFmF/Ge9vnsZ+yPkz3UDbaOrjzpGYd/paP/cjRfoyR0osgm+FcX+OBMoauwaBXnIFYYMLu2RDzT8Vjhl8IYxdt42HyoJt1U5v/ikP4k2dcEDFD3lJEKqk3Hxh/bJG1Hlu90NJdqNsMWh799wOmDU2TD4TdqZMqZrGD4fE04H9Ck+B3H+AsT5r89t/EP8xVRUcJlTw42auVij6Ctkr0iVT7Ljs0+myu9FJKeM6DUeWc4UZ/JraXSm0/Nul388V1/4ArH2aUwrAb8haf/PL/x4NfoQ288iqzGvlSsKHhsbpf3DgBlTFBKkSnFZa0Wy6XNHThz3ppn0hwLF77kIERozddTY4a+xChmjKBTXLo0Knqq9G/0VB+vzbfDpDVoLbhkGEOeKf6CoEObg2Wp5orVr4lO5RU7MTFfL0Yy8+sOhDOdDEltU7HQcsJjwrkR6AxbBy8VWdmkpc7wCP+CMyKRYkFPS8MQ1q5j2uvZWycDyB7cnm15tLiP6yrjJ4LfdbhtD5VDKouVdBNjglL4COI99EI+jsXoSHAN1kmNhTBtGOSW0+8lUmu5v4etUldYTokDPwAfIA7ETIl8uTHM+edMWGj2AgUfGGBCxoPM7IsLdKHmzOVLwSl/Fyf4Tcf0FzZJW4n+nY7hxpjN+YTbv3qHtiv+olOhOld+PjEQl3k2XxGbyqguBAbPD1i9vrLPh/XWBOqnG/rbvfmtxNK5ngHkav4sTeksKSOMKkks3lSy/btrPPvhwhr5+LE/fn8gC4d1HdYpfObiZ/nU1mf4rfhbNt+59doOcrKW2r7nh5lzZ8xD7MbXbHJ28dbyi9oH68eeuFLyQWcu1uXUQIXswE0snV6yPeDai7WaPeAta6N7p7e9+/PBRoKYDHtoBFkZk9Q1AR3YqpidNSxTPCyL94Y4QYcmgtPeAEotp6mu/TIyaGf+oOGCFADYbjxpbD0qIgW4CfgdCJ+qYKDoSSYvMaZpW8yCW7t5aSGLdRx+AGnskKlZ4VEId6pCdc3h94PZAPoMWw6/5YuYTJ5QvYH0OKFLK0TrcYtS6HMF1ccDV7Pj1g8isVViEuI6tU6BpRUOf9GfsUxlrUQ+U/BinUYRyrkdqC5VLL64JK84szvQcs+s+3w/dUz/2CZ5M9O/04HAU1ev8No/f4N22SKbDr/jcROPjBx+EsyxHjW8qbHCpb3ixp76hdoMsMtn6coITAXNUwO6G6uipMU6xbJVW8NzuSv3ghdin1AyYa8Cpzjv2Yqb/MLOH0NcsJv0DzuO3+cSYVKP+eWrv8xXD7/KUpecFVxPqd6cYQmUTQrF9cvEZPbPIg5ty5MIkGxd6AuNVwsLM3eFKq0Kq4zfrEg+noGRa7HZOssWyWgvtK91aFSaF2tiyVLBKbkWyyCpGnKV7KavhbDtSB6Sf/LaJaj5dvRifIq4nuPf03Vk2xjlh4k+O3woN6HVCRuTALyjekHwz9XkRx35Dra27Y37Qeky1psWqQS/ZYZHHDpkYO2zZQEJsgR/2VFdqvAbNfGVFh16wpYrdHpnNoSV4IeO/pbS34qmxi0jhaHV9v5JpfRbDfJhuLCM3Ht7Rd/aBswaRfOqSNmaC9arzzKmqAdxdrK7ooZ1UwhXKvJBNvOkdRJfwWC08CTcUBj/9Ai2Et07HXmhDJ5uSGR6IjIS/NTyd10jJjarBKkCmpNZOmYlVN64OKpUOzXNc5H2rdbe26xo7fCFKFddbIj3yyq5bLOMyVpA5/VBhBIfJuJhptqukNrhxfF5/3k+Mn35rHv+cTcdf4ALh5Rq7PipK3+Ep19/iteWrz6RmAdngT7rNz+X05mSeIacgXVn4GhUiGYeq0lNVl470omtNjUpeZbPDGFJdor7obfQpI6z2bpsJ41jgeEq8U4PtcUd5NCbSXIQ4mnCLQQZONzKTGwQC8EW9aSUyWXr4ze9NQsO9GCd4CY2H6+LByBJ0MfZFkV5vcUxDYsMHeF5D9nTf7Ulz5PdmOk9d5xY+y6NrbfdhuD3HGHDOAWAAZ8A2dig1ZWKsOXxwbO42eM3rNC4obefpZayRnT01xdwkpGps+2Fl6KnKNun2or6o0EmNomw7xm0sDjMpM7e0/HE0ezCYoAphgsmtPYKcdUaEFXLkN3yhHOB5TutFfd1h7LGQkrERPNcDXuO9nZPfliiDKaNZbqmMqJMHK7xuODOOiBpDLBOR8mCtKb6pB5KZuOjUw7vdaQy2omzcVi8EM57dJXI5bBbj6cSKCLG4uiGBXvpxBNXmXpYs5G2+OXpn6euaspL+cOtyu97iYBm9kbn+NlzP8vrN1+3tZZio0d50zXb1611JbZO0TNc4qxoqIm/qlCRoiXDpz6RjuMZkMoM2AERiw/UmKmvVMRFIs8UnSVyOQHLBo41o0kGQnyQSI/mVC8OjSTVGT2+faMjfLQyt6txIN3obPb3IGNwWUgrtaSwrWw5JDGRsyBaeA2ojXChjGHJlRWzbWGkEtwU3LMBvZHpvtZaB7a+u2t3FuFAY6tI4zEIfoRhIOJIj6IV42AFSsZQXa4II0cYV3Af0kJx24IbF5r1mgSFYS39W50VM1cKlFLsAZxly+ZSzCeK2xO6U9jecmxuJw7uwmoOfVQGQ+M7vHf79ENr2Nq2UW4g+C1vRLtlfrIeF3mCfznwE8FdCLSvt9DZKObGDonC0evHBiQPBDcIxvAsI5FzgnjFjx1dhvSoJ1wIZzwgzRlGnu3P7XLwe0eFDmAnjGscOKV+ekh7Y/XkPvXgBhWpLaNtAepxVsxFMxWen0s/zUc3Xz4bxX8cAUz/+vUHt3AoIMXrwVf80tU/wd+//d9znI7JufgGFe6AqxzqzKVJ1zyOdUtYvkbTmtYtdKsejzdbwJPebmCvSHSkg0y1X5F8D5UjzzMy9FQjR2oiubaRIpPJayd1yqkzdrDKpMeQvzKnfrFBRsYZWb3bMrlSW8fkjQvRv9Pjx4YPUGekE+KdlurDDfQ9xFIwJFtD4ZyteTNWTM4eCBspGDqqax7pHasHHe4aCN7aW7FNhRTmp41Mhj/IyOM2BX8+0N5YkQ/UQrdVYSDUT1f4HYffqKirwPztOTJS/MTbiFKXP1MEUaW/F+19qwTNvuSn2ImvqZzEBdBGQc475J3I6VLZ36zYvRyZncByljl57JHzUgqnnClR192SGzn7GcbOqN5jD202fsjagtHZHsI5JVyq6K8XC8gRpVOC/l5P13X2e1UptN44JnJGi3eEPYuUyCssmT5BXhi/SFeJvjY6AAvQDet4TRphK9XB5QH9o86Eg0OPtslIcGq43JqK7oKgtWMzbvLL2/8xtfgzwuH7KZ//d11/YAvHk/dFgMyHdz/Mh0cv8NWjr7DemIiAq9agw5rXYN8jlHstrU83NVelIrluj1qbvyuPNAYoaqVkSfjUwMgIOGdA14Yv675oFOVi36fFewGMqxAX5cNthe5Gwg/AnffoUOjeWhA+UpNcxm964iDR3+1LyLBHghIfJJpngYm3FtYlXG0+G3meobNpSZ0YYKt2AssGhHOBsBFY/s4Cph4pDw74J5aUjjOcxdWFOr4hVOcq3Miz+t2ldXHeBFn11UB1qcZNLAmtftTw6N4BfuTPnLakFtyaAp+gu2F4D9UTMty6kJv9QDlVFQMaN23Eah9nFiuhCUo9tOLQVtCLvKc3L62+PrE5lOJl6jecyQ16nmBh5f/rRPHnA2lpAHt1MZiGp3Y4F1jdXNm6fWgjJYW1S+XOaADiHG4/UD81IN3oaQc9Kgk3dlQ5kMXjPDjnictEXma08zaWBgPm3WVnMoqcSHOjlUsFOXucqrGbGzGLBw9Nrtlrdo16fvZs/GHH8SNdCkyGE37+wi/y1YOvPTEuybbj1/UptO7o4ck/rEliWUuCvZyNL3mRjPWpSnUp2APulX7V0TQNOoA8TwQN6GTNdAQwoxoQGyWyQDCSkDtWki/2eKtEnIPcS4TnPL3zhFi2JAOz3qNT0mGmP+lNWeoD3e2e5uNDzL0Y1GVco7iJPMnxWAOi2GkbznuqScXy95bkTk3GfwYmclYwXPWek3oiyIYjTALNuZrVKyviW9FeG1Bf9VTPBfyGww08kzjm4OsHhrUU5acrWTXkUqN6R7zeFWKdWLFeWaHXuowZHmOsFsBTavA7nvhYOZor+5ueUCVSLg7wxY/lrBi49c9mGIYLZl/otz3p2HQtZ7olCpC95WFgoPLg00MboUoBrX1F13Y2HowLJ6UBVwq6G0KoA4IntKA7geOvLmjfbXEbwugzE3SMFQqfGVwYcPraCfnYkcaJsBFgaO+/CNQfGqJvLOkPE3683hYVMuLahFqswykDqv04P/56cXZ9IAqHgfHCz13+Gf7Wq/9vHi8fkWIRgiGMpiPyJNP3kdSaP+OTGAE4W4OUr5cSaqPYmjFcbZCc8OcrkExaZrpZjxuX9PfHjuHzNf0g0lX2ofZJLXC4BWntKJWxe2JTB2jnkJGCy+awHkGOQcYCI6guBvMgvZ/IjyI6g9Qm8vcSta8Iz1fQd+TgYahIpyVv1V63NDa6uIHghp72jRXxwEKu8ZRksTKzj8ssX9p6NzQQ1Q0hDK19n//6yh6okcM/5ak/PiDsVeaiFjzz78+Yz+a2VnWYVshJAacdbiSsvtmRT/OT0G3h7D2XHuv4fOkJpfAXsp5FQ/YJ5m1gc6Koy3SsP8fy9ev4iDK2mD9qxk8DWllQtIV4l60FII2t1fOjiIwM5G3f7qy41hAaTx5a1+kn3saFoeBGgh8HarUkv35uoeHtbQs314GQTmH5jTnixoR9A0/rvYbwViDNEjpzxNNkI6kYwI3LTCcTDk6ObLwTf8YvWWNn4jHPl+hwbo3W/+SuD0Th0LITf2b7aT42eZlfP/l1pM1kZwDUbDmjGTY0ezUx9PSrZGSsInMXzT9kr0Yh1kA5mdtIVkdQM7vR6EiVbTrcyLM4mDPpRlSTGkkr2kkmd2oS9VrP1n4u2Ax/BkZ2qQCSDjfFHMOOEuPzE5Z+hkZPNRHCjie3AT3MxMcJfZg4/d6c5ihQv9zAIOOStfuWj2KhzxIUV3bT/Ss9/d1k9nXrrmLqjNY9dbipLzO9Cbr8wBPEk+7B7EsnrL7W2ji37fD7nuYTQ6qLFa6x148oq65bV/ESeYmNgsHjB47++z3zbyyQbDsnJ9aJSAbJ5b0pvGwtnJyCY3OWJ4NyNEuMR4HxRqRLsJzxpGCsO8ZyMKiCrx1MxCIrlvoeDMC8U92kGBMJ1M/UxiM5jvhxZd1KbcQxABmCnzgDR+862rdWHN1ckmNm+GxDszukf2RGPHSKDCHNlfk3FjTXaqqnarrtjuGlIbNbc/MKPcmkjYQfPukAm0sDmns1UZJthQrPRNaOaNj7tpk3GGhd4kt/Ek+bXR+IwmEwhzKoR/yZZ/4MX3r4O7Ssih+lQoTlqiWtEsMrQ3TQEbM9+KZVec8fJbbG0w0hLWz9KqNgD0MryMQjUmzcCj7St4l4L7LxwgQdqAX5rJTcPPmDZeRsJi6nqqgBs8yUPMKAzQ2lj4l4p6N5akzeTMRJtIcvenRX8eczaTcitxLdYSJ/c0n1dIPfMaNjFUUm5RnrhP56T/f9Bamz35dQQL8thz/n8YX16MYOXzqNECrCYcXy1QXHXzs1sFAUmTjcnqP5eEPzjMnHpXRY2iv9rC9joT6R3HvBD4X+9Z7Tf7VA+rKJAHuRiSeA5hrctA/CPtj+yZeuQZCM8uDQ8dTTIwbacXxaPDzfM31KKrgVirvo8du+5MXYZ5adYVPUlEQ/cNtCc7WmP+gt6nJ9b3nbfBjFx15gvN4y/+rSRqrGClt3GknDBelxJi/sm/2lYIX8VOgfRrIoVfIMpkP8QGwNPsv0D6PZ/u14aIQ4jWzub3BwemxFY53J4+z9E3GIeF6uXmIQRk/A4Z/Q9cEoHKy7U+ELl7/AxclF3jl8+2z8WN9R3bzH3xSqSw0MlKjRTrLemIOswVMRmvMV8X4mnSREIqMLY0aDMUu3spvaU4Zj+/Nn7y6YXBsjxchWQpnXywrSTQoge8YINKal9oosgYXN8WRl3q/wb60Y74yYXN6g3erou54csxnAbDqk7uBdyEvo3mnR60XV2nI2CuVZNu+MNhdxlSvbAsHvCP5c2TQMS8GQgN5OLG8uWd44oHs7kh+nYkTj8Ocd9Ucb6hdqKxrBWXiQgrZCPC6ELmdu8GHqYAWLX1/Rfq81U501WysVctkZ4wsz3imALiUGQdUKAG3Ba8p40XWR27eE7csG8mrpNlRtNNKs5rHijfIdtgLaJWPCrnNJguk+6NUcuraEauJZvbWysWFlq32vtrEwuYD5YcglT/10T1yY4Ez7iN8NxFuJ/Li3zi9APkyEPY/3JqDUzvg+cdwzvTpl9nhRijzEg+LlstPQjlsml8dsHG+wmM8NQP2hAgKNq3jGP2s43k8S4OADUjgEoKyhLm1e5uf2f4b/7uAdRNN7Rl7DLJanK9o3Owb7Q6ppjfhE9BE3wwhj0U5PnKO6HBhMx8Qalq/M6BeR+qW6KC3d2TyOE2YPZ5y+MUA/LPbnIE9ALMANxMhaRcNypj/JoC2kg0T1QjDkvjd/j9l8zuIHKwbThupaII0yuUpI4/ETQaY9/Vs9uhDCtYqw48iLTHyYyA8iYOtSKncmoNJTwzU0WpHRudHX+3c7VveXdLd70q1o0QYAQ8M//KVA9WxFfbWyrJSB2fOj5X2bJ2hN0FadrxCF1W+1dG93RowrQKNts0prkH/4QzTDaTlTNa8FfqKOeNQhKoTgcDmj2bFa9Rw/8AjOfFXWmxlv76v0+mSrtMYG6jXmUghfmpGxRS5Wm8FctY4jMnDkVSaX0CypwQ89fmjRBDQw/OyE/t3Wxp06kE+V9HZnsoOgZ+Hi6TQRtipzgwulqKHoU56GQNcqvuQA0yrdo454IHRVTx0aO/fW99I6IqL4ddxZ3rdOqGQF/6SuD0ThAM7m1uArXt76mO3pSU/4Ghkzn8mQYia+NSNUDh2Aazxam0jJrOuVeNAzfHGAmwbab5+SxFafg74IvCIWibimSWfh8PsnbF3ZImfb1Ru5yvANGTjSwZqhyZlylWxtcrwTcblBNis0JcNoensoF6sV+m2l2gmEK7Wluw089XOesFvTXe9Id3tEG0YfGaMfzvSHkf7dnv5uj55m+/95yoMdCFeDuXpVFfJYOf3+kvZ6b8n1RWgmteAnQrgcCJcrql1vdnc1JUXNrAVIgraOsBtQL/SvdbSvRDuxnTFxKels664BYO0zApyNLFrGOMoKXRTolHxqvJEUldrB1sTRd8qwTvQCbS9PwO51jmrUInS0/+Yqh9/wZ4IxVG3dOirj2mYgHpZuobymPM/othKGARrWEIw5ho2F5vkB6bAvGzkD1zWZAZDdl1jg9iwZi7YyEFwTxNjBtofbvckfko1Y4hR1mLWhWLiYX3NHyhWCJ2dLi0Md8kNV+Md/fWAKBzwpHlc2rjKoGpY5WseRsfZV16i7gWLdKiFz0BjP2HpmFqxIXRF8RVxGS1nPGBFo0Rf7PswAOVubqGRWR0v6d4bonr0eKdwRKavgeKD2kGU1SnrGFK+bgoyF9p2O4eWa+vwQJRFPkp3mneVlxJNI/72eMAn4C8G6mG3H6OMV/ZVA91pH98VTqq1AdbGienFI/khDOs3kVcYNPWHqqeqA7xzpfmb1+or5uwviIhpmOy2gZjBbO7/vCReCjVpDe8DC2CPekddOZVHprne032iJB8nGL4+Z+ThvwPBahKb2/p511uvPI2H4zMpW0TRqI0jKyGLNmhQEZWfHMx3A6XFkdh/8ORParblsFEamucLpGVaRM7ZFmjh0pgYSV8bLkIEQRp7ufv9D91RuM6nN9nPWBZiMxbZRFN94dFSRltHwk1BsBN676CheqbYmz0hvcog0z+SHyXDcTDFNosRTOpwr42WjhQdjxdd5Z/GQqlTZP9km/SHG8b/8em8+5sANcG1RQ2o2oVHx35Ty5toDXXCNwlA843iInaT1rKYbdDY/VrwntKdwEDqTe1v0gYITjr59wvDzA2TkSKnMySWrNB31pqBMVjCYCL6BcMFa33Rfie92+KFQbVdU05rURosS7DO6DOSTRDxNpJstOs9oJ7iBp9pzDF6s6O5F+oNI93oPfcZPKuo9z8b5MWmpLL4+Z35/TlpmtLdIAASThTu1zmAouA1POOdtwzK1zJSw4fBTcxTXbLyEtEzICjNyPtAnGbmucELeM5OvR7cziv8azyj3u2YLhc73EzIWdGibEE44UwJvNEqtPe0is5gJixn4oYIvCWmOJ+7rQ8gnyVLiNoN9zrWRuJI6W4M7+2xdJfhhID1cnqlSyWpBXn1Pao0Mplok8RUmnnO2zvbZ08/6sjIt3YEr25vi0+Eq07cQMSPp4870TgP/BPPKZatXeDWWQezxLhDbhKscYVCV2ujZDFtP1s8/wesDUzjWlwBN1eDMRsuyOdddx1rHcgZqiq26xJnl3XqsiTbXdjdbxi+O6Lc6+t5aQam8kZlqT172dnom86KQcSDPldoFujah8wzzDBsGCOrCbmzbYojdFOcDYUssb2Vg9PP+foQEzbmGalhRjaqz9SIoOdlp1d7p6N5Y4XeE+vkG13iaZ5X2QU980JGPE7rIrA6V5d0ji43s1bY7ASS7YrGoT1Z9I/OhcJuCm1S4gVkL+C1voi5vAsC0yqSF5d2mu7n8/Jz5WkgRma2Dqc2yQM7GBloDCTXqWfES1NLiMqR7yVieHlgY3hI0MwT6NhPFc5pg6DIpB0hqwGIEpxltylzUOLrrHdVTlT303lioeWGmSVksUc6ybzLxqKDXfXlfpibd79V0TKwor9W6ChkJYdMRT6G/1bPW32iQM09Ygtiv2sa/9KC3Q612yMid3YprnFg9Zv5c3i8XHA57P/3IIktNKihs17vW2Z4diT+Z60cuHCV0+uvAbVX9syKyA/w94GngBvCXVfWwfO1/AfxV7Cz+z1X1V97n1/3vvhQmzZjKV2iPbQxKIJKuAfzySanDRGAOO2nzk0UJCkd3D9kZ7rF5dYvT5pi8NMDL1RZhkBW7+bHC5AaedNyjrQUo60LJK8Xtm82bRke4aNsTGQoMhXApIBgQFy56O9Wy0t3raG+0NPsNww8N8d5bmE9Ue+A31Tw6LzekWQRvRrhSO6pnA3p1QHvY0T/oiEfFlb0vgdZRy/bBfs6zTqMpRLCRMUfdwEEt5r49dOY6njO5bETW5jOr11bQrG39AVfEcsG9Zy3LWfcha91Qaye6dobBqBpeIkWung6ywUfFXiJ4MfzHC7PW41Pk3FObPOiWuEUkdrmohTFCXBBc61h9d8XgMwP81IhpbsujtxJOnS1XRWCh5EeZ3GZ72Asl3m14GEJd17SrzvRHyQpBbnN54B3LV+fWcVFW0cV2UNYGQuvuq2QDn6HmylmhNdMh98QXJJhcv6oD2inVqCJ4Z0UYxakwraacJd39Bzqq/J+AV4CN8u9/A/hVVf2bIvI3yr//dRH5KPCfAi8Bl4B/JSIvvK+J9b/vJWyNtji/sc/jowMQs54HyF1xXypfR9G0IBj1WQouUWjqqsKj1x6zcThm87ktVhtLdGRKRskZ75RYEHqN4BqIMZHuJdgpAcwrNWcrHP6KM6xgXFLQNw07oDPwlFbpH/fE+4l4qyc/yszdgt1PO3Z+ahs5l5mtVsy/e0z14pB64ql2K9iuzUw5WsaGDw4fHNUg0O/WdCeR/lFLXmiJgyxaHl/a9KE33GJgIqon74vaCjDYaGYEMyW3ZiOoUckHkXikuKEWeTxnD4iUtl0ctulwRblbQrhlZJm0urACTHQQFS8WtrzGBdbsUweECmJR29YNPPJzok9UexnnIS6VtAS6ZOZGmuCR0L3SMvrCoHR8ZnasnbE7dZVxexXxQcFnBFvR7hr/I1dKM2xYzVtc7cg5IZ2QW6GPPfkA2jc68kk+s3CU4gkrgaKVERiUEW3tF+vWeJJhL654c1jCm+XBusrTuJpV6PCVh9JbGMYBj5cP7HfWjj8/oetHKhwicgX4M1gC/f+l/PYvAz9f/vlvA78B/PXy+39XVVvgbRF5E/g88Lvv26v+fV8sjKoR50bneG3x2ll774YGIq03HqyDlgspqECjdpOWf1RvqtnjO6ec3psTJgFCNo2At1PZaIa5sFBt1l7eWRFG3hD5Tg18G4LUSjpUgnoYCflQSTcj6SiTHhutPJ9AXgm+VtzUMTjX0N5ZcPQvhK3nN6gue4YvT+2TixbII+tuIDnyo8jW1jYyElpp8QPPsGlIOyNzRCsgcMqKR2iqCrwjS6bX3laFWc0DpM8WlL3W7qRkG47WfglC+07ChSc2hipl7l9vS3I2FalSMA05EyCaUEyR+kk3iAr5RM01HikJfEpolDBQ2lqJLuHI5E2HhJ5h7YnRsTqR4sbmzPy4ckhtVPH2lSXNx0fWEY2sk6KjrDLt4e4e9pYlU0DIsOetoFdQbQT0LSWm/EQfUyn9sWP19ZX5xXp+CJtYWxK4qVHUpXFmyVg4PlIXF/XGGQM3FK6Gs1HYN55haIipmA6Ry+pZWcd6vKs3Wfvo/oc4qvw3wP8VmL7n9/ZV9S6Aqt4VkfPl9y8DX37P190qv/dDl4j8NeCvAVy9evV/2av+fS+lrgZ87NzL/O7hl8gYUi2VrV3JiVQIWBKk+FWYqmXdNhpauDaFsZEmR6U96KAIihQMxLwyINIbsSkqrhLSSaJeVeTT4vtQ9Ba6EvQw088T3OnNinBueaD01hFJUEZ/dMjgj4yoL3jCpLITX2HezUmn0L/SEt+NdO9E8qYy+dNjqu2AeEh15s5r99jYmSIXbFzwwROco2pGBGfkCC+u7ChAs9Jmo5R39HYD9mUU6croHRKo+WwQbR3Z3+jI9zvU+9KV2Fu3dgdXryYBr57c0mv/TxRYQT5W0sNo+ScVhJnS3zQjIw2ZQSMMtyNhomgv9K3QL0F6cDGRO8fsnpCjs16yxQpYFlxWyMaG6+9mutdW1C8EXG2CwPgoUzk7PMRBPO0s7iKA3wm4LW//TYQ8yrgo9I/jmbQ+LYT0gyXxYTIiXGHMSjFEdk35NXUl+tGR5hGasskZmR5Ihth45A0TksbjR4Gqrqi7wNJ1qJSAMYeFSqvQ5IqX6pcMi9GfZNn4EQqHiPxZ4IGqfkNEfv5H+DP/bT+B/hu/ofq3gL8F8OlPf/rf+O//f18KThw/dfVn+TvX/w6n/SkuiGVQrHNTmgBS0t3XhYWSZ7Lelac1qFeQ+n+9oivGLTiyLIykPdp5xFtEYy0VeoqFKAWsAJ1mVEta/ApTaXbFkSxbPEL1Yk14sUJzpHustI8iOlfSw550KxEf2JZAjdKIn3n6m7W9uOBwIaOb8PjuIYN5TX1tSF9HmroqzZGjdjWuULe73NFpT58jXYrENtrGpE1lY1L0HE0hWWWQTuhe71heX5VoQkr3ULZPwcYb58VwgsICRQp2uF5pCkht7Xc+7JkAO7HnaBw4cebDSpNZDBpUsuWMzBW/VJpOCSPHonPkaEIN8WrBzquIdlg2iy8QQKcsfntO9mOzQpg4uJ9gUOwjnSMVc2c/dvgLrhQNwEGs7T3Py4T0nhQz6VaPHhf8Jtj7TwXUNp7K0Fi6bsfjnNrGP6l1IsWD1TXvwUGC4AeOsOGpq5rhsmEW58hGMNAeMwSK6vAeXswf4dObn7Nj773pbT+B60fpOH4a+HMi8qexuOENEfk7wH0RuVi6jYvAg/L1t4D3thBXgDvv54v+/S/jVHxy/2O8vPUxfvfxV85AN+0tOBlnazHNiUwhz6xXggVoWlvkn9kESpHdF0ImquQM/aOe4XhIpDdnKYW0TPhRwGtAhxG/4amGFavgyFXpbmK2AOtsoCzJtBDxYSJ/aWnUcCf4xlFNA81wgL8q5GcUsuKmZpTL/6+9N4+27LrrOz+/vfc5d3hjvapXg0pVKs2zZcuOZVnG2Hg2DhCIs0xChzgmJtjdDivJIpgk0DiQBJKG0OkVerk7EDoJ0A5NwM1KjIkd0uAQD/IgT7Jmq0o1vhredIdzzt6//uO3z31PxiISluuVVPe3lvRevXqv7n733vM7v+E7lIZKjS6Sxg3NAHA25BytjElVonNVl0FTE3yRZfWHhsL0AQHGTUXdmBRAHNmAUCuFTRh/dYzreLqHukiE6nTN6IERzYWEc4qQmbZCXh3mz8lcHsVsNNvZR0ZNSlLjczQgKVGIkgicSAK7hc6RgnCwQJat7UjYuIIqouOEW1X08YZ0b0SaaOttIVshmDiQrcjz6jsqzaoy/MgGxfWl3e3z1kcieBW892ivIVzpcT2f9VzBEPL2uOmCohuV+cEkmcyJyErp0qq1z7bWno6wy0NU4jEbYkvPWdXRapV0Hb5nWqy+6yh9Sdh0rI7XbJsVk1kitDAAEbqpw7d33sKucg7VbNJ1KSFHVfW9wHsBcsXxt1X1+0TknwDfD/zj/PG38498EPhVEfk5bDh6PfCJZ/3kT3Xe/NzNdef5c9d9F5+98GlGdYVIMlEYMeCS90LqO9zQ7nym/9C+0ZhgDwhbW5hWcUySy3qliSZFym6XYRgapiL7fdRNw9yVfTZ00w7l1VCGD5gk/6RFapINZhGzD8C4E8W8Z+b2GWS/Y3R0xMb96zQnDDDkvOD2efxuh9/jCfu8+aGU1l65YDT66D2pSjSna8I+T0VtF0rmsojUiBNSnmekyv7TsSlqewmMjlWMT28yvndsF8ic9e1ejIciku+E3uFS3mgEjMyGonkVa5uOPJZO1tZxtqE5WhEHtlxs5hKdl/Yobi6QbnY3i0JMyYyo1aQR6Qqx72FfoHtIGf7XAemR3C46pegJ1QYTo+h29iAxoQOhPt6YfEBhsHn1xprtHu4xrka28cqYkPZSdOue5qsNzVoydKe3gaZVW7bOlVIs4c85A8/t9rba7jiaoxWpUnzPbYkkddwEhOY6gu94uq6LXoD1esOg/s40Z70CTkji8BqZS7PcMHMjuLBt2H/x4hvBcfxj4AMi8g7gceCtAKr6RRH5APAl7PJ498XbqECbeJM4XnfN6/jtYx/kv535b8Y3UJtoOxUTs+kL2odUOxsCivXOKWzJ7wlqGpgZEGS6mWolewHhQGA43qTwgfG4pljoIL2aWmrmru4zODM0oFRf8Nd43HEhbrTDVPDzDn/Q7q6uA4yU/qE+4UjJeDhmcN8GzbEEDZQHC8LdfYrD3iDheW0Xx4m4HklnI+lMIl5IpAsJGYLre9yso1gM9G6YoTjsqIsmA+DSZLajY0OnikIx7JCOR4afGzB4YAN/KICz5yfMOeg7XFcMni4GQJOusyoqkTczoDG3KR3FiyKjRPN4ojrdIGuJZpRQ53BzSnFdB384IIXSPFSZvqqBN2zb0HNoPydmAfXZo2ZZmHlLj+rRgureinjCWlKHmK9stkqYDF9FcY3iF0p0dWwyf8D4/JiZa2cZPzLali1yRYHCiUQcRZzLQ1Cfoet5yGu6JuB2mXyg3xOM/+IDaUWJq8mQvt0MEejmNiW3KqFT4jcdg80hyZulAiEbTKtVmdp6Hgtcna5jX3/fRbusvjaeUeJQ1d/Htieo6lngNU/xfT+NbWB2LFxSFrtL/PWbfpCHVx/gZHUGaFsPu/DTIJksX8+ZfePQvF6ltjam1XWQDCCbIB0zus8vBcLeQPNEjacwmrYIYZdHSghliRsb30MKs1lw894c2WehPFJSHC7xuzKo6kRk7rZ5OCBsPrJOPKkUezr0bvK4BW+bALu/w8BkBOvjDfXDFfFoJF3Icxslq3AJlNnWMMD6p9fpL/fp3NGlvDKgewQJRklP55V4MlHfP2b80DnqVTN0ljHosUi40n7vtO4J3WweFT3xXIOeaswfJdqAU9SSSjzXICL05swLJK0bLT6W3jYtfdtcdP5Mh3Rc2fy1DdL5lA2qFXqC9NS2WMFeK+kB3SwDsCDWysw63G7ovqEknkk0X6qpH02wqmgmFrZeO+qEeC6Cq40b0rUbSTNQwnxBsRCslQJbLwcHw0hzskadtRg2CJEtsFuwWZbfFXD7rcrwu4QimHDzeGWcBY5tLet67frVQGCFL9CVhsGogTKTBwV84U2OMG+bFNvyBUpe3fk2OqG7E5cW8HxEjub+GgdOlZdd8XK+58hbef+X3k+tcSJILEGsPD/XmA1j1+P6njArpDoamtRBGud5RGqziF2UruNxe4yG3TSR0YURxVJBc77C7/bW85YOP+Oh8AbaKoWyWxD3N4SDJWHZm91BIcQVI9XVRU36UkN3sYe7y5EKJZ2LNE80xDMNcTPRnI/2+fkEa1ZdGE/Ceuy2FKYDrpQJilFF2DwxZPOxAa4jFFcVzL1+juKqktRvaIZjxk1Dk5XRBQNB6UBJ5xNuQUhrkVhiK8aW91GDbkaa1Uj9YAMrhsKVjqO3R5D1RNM4qqi4uYziLQS3SyhfWuLEsfHhVeIFRUfRLvI5Gy6SB8uk7G2joMNo1g+NtWbqbQgpuzC6/wGhXO7QHK+JR+PED1h9i3VQmpUmk++sIolPROL5it6NfTYf2EQ7Dte11Wl9LlKt1tC1bkhLA9JJC3SbAbfb4ZY8Ycnj+kLhOjSP1IzXx0gnq4aVeYPTEUI3IM6hQxgNR1ucGdcOl42uby3lVs+kSTlSHebufXdf1JnG18bzLnFsDxXL+n/x+r/I7z32ER5YfwDIfXkriBKMU5JWI82ZxqwFZzKJbZwmVojtmrE1yZJeFvCtC+LGkJSUaq3GlWIU6uRI3oRqicm0IYJHlh2lK/B7PWEpIKXQnG8QL4xWxnS7PXrXzFIXDcOTA0b3jqnvb9AIbm9GoI6VuJ7XuIP8C3UwGHM3f94T0wzN5suAidvMC7pmGJLRIxXVvzrL/Cvm6L9yhuLVM/Tv6DL8zJDNj49oztRWcTTYMLCE5G3FrMmYpZIM1s+iABG5VojdhIwBlOQMLl7HhHY9qQiGX5iF4voSVwqbv7lBWjWpRWbEBouFTLZRuG2VnmR0qiYI5imjUdFzCc4KTcKSdgebY8wF9GwDtbVFkuUIfenN+S8JOitQKcOHxyzcssho0QR6RO05lXWB3dC/qg8zSlpT4vFoJL5kHBaZMyEk6UARO4y+OKQeN9aOBMN8SMdmT6KO+nSDNtFuQF0/8YFxHRuQhq7PwkxbnZOo4BrhtZ03sFgusp3nc7HjeZ04WgPeKxYO8tZrvpufue+fUjfjbECdJf0Kw/zjXGahJppR5lU7rDR1mJ5FtFWtiH0en0iMz6yTmiyD1yRrX+Y8aUMZdoc2aY9kwJniFxzRecLugHTEKogz5gTXv7aPLAuDwYDmgYbxZ0c0R61l8ldmTcraSm/JxLokaqV7Rw35mfECbhYT1i3IF15OerWi8+AWEvGJSBrD6ofXGD1QMf+mOfy1gf6rZgnLno0/GFA/0pgE4jiRxh7nMt1/E3SBPAwWW4X2xbZDY9ATDVo76k3wHjNvyvR6euD2Cn6fDRyrh42RKvP5rltsQ1W6nOS3IHr5tXG52sBg5oKtOpPaBm1TYQNU44QpK00e1BagIc8N1H4Ht+AYfnnA0qt2UR4qGZ+osxyf0gwanIit1vuOdKYxacEiEJZLZAl8aWJQznmqh0fUq7XJDbq8dSoNTp7WEnHT1L7sd7UEaSrngis9oVfQ6ZX5TZz1ScSGuzfEm3nDFW+YyCruVDyvE0cL4XfO8ebr3sKvP/RrPLT+VSRlkZqWmJIdvaSwgZ5k277tUF7NTmMTM+CoVGdHkAxmjSak4/DLJW7GE1cbhhtjwl6PKwyv4DoOHSb8rA2+0makORWhSfTvmEVmhPpcTX10THV/TXPKaJr+YGEtR2vl0Ng5Adx87rNL67/djLmnyYzgei5DmyVPjNWQn8OEW/a4OUf9UIMOHKNHxlS/3DB3zwz9b+3QvWOG4kCXwX/dYOMPh6RNkE0jhtFRGEWrevrG7DWnd0G6ZqAUNxysZNBWhDS5SAzH4BY9eIirVgHQdbYValGXLQKzZZuSv4a9qMaEdfZaSZq8joa/IYv0YMnE5d+dPFlshDTMpMVow1y/zzF8sCE+WBNeGmjWGqg1S0l6qhMVoy/VdG4PxE0bVKaNSD1KcMKIgd2rO5SUrJ0akERtMOwzNaVJpDVDsUrHZjUut5VmICUGIhN7HkspqLUhJuPDSII9aT/v3v0/sqe7J1cbO1dyPK8Tx3ZjmgPzB3jzkT/LL37pF6ljzGxZzbcitu5ehYM62oAvk97aFSYxT7bzDZyYv6Zk75I8Kfe0TBi8d1mhW81qUdXuTnWi2TS3uO7NfWQGmkFDfbIyyb4VA3iFI8GqhlrRJpEqSA0GrZ4Fvzcgs3ZLlcJWpW3SaPVAJ1BozdXKhiNtRGTe0bmjpLq/Il0AHTas/ac1quMd5r9jDrfkmXnjLJ1DPVZ/6zz1GZO/I4GbCVlgJ8HQnNIIVim4DuiiozlreiixyrYCHcmoSZuNkJQ0MgFoCkvcLb+DSeLY2h7h2vWvvQbtLVfUGTmtEgPtZWQwmtu0LOREtBZLGmCUbwJDNcj3rDnPrz84ZO4lc9SLDWkUERHKvYHmQkVROnQAvgB30F5k8facNqcaNp5okP7QfGpLazukxNqmIr9H+tlbJSuoucJZ9RHcZK4RnLWATYqW9FS4ojnAD8//De7c9SJatbtpq3IRwovwPTe+lQ+f+DBfWfkK5Ak1+U2Y8k1ZgtogrtGJXP12nRRJuSpRK4lTAuetPfDzpt9JMCPiFNRcyoKYpWSdSFkdSmtTt+7sK/G7AnEUiacamica0nk7W3G4QEpFK5loVerYvGL8zSV+r5HE4oqi68kQinscstiaINvZtPUQaUWHCqDEnOtnHZ27OjSPJerHahgnRl8cE9cjC39+gXCooPNix575fZz9lTNUmw2+rzZzmQWthDiIpJhBTflCdTM2Z9EqmZJ7RzJHA7uwnFVJaRCtfXFq4KhgVIDWIJsWiJeTxgTFa1NDDF5msn6aJDNuE9JaYGRhH0lqsPY6SzcOLIlQK9LzuLohHAis3b/G3MMzFIcDo0EEUWZvnae7v8twPLbZV9WQxqCZxKjjLOAxBMotg226mIFTJ1Poi9bP1uELlynz3qj+YhWHBEdHSoZVBV4pJPCC6gW8c/cP8oKFO9hOqt3JeF4njic7WjmOLBzib73gb/P37/27nB6cQcfRVKwycFTFek0Vsd4YndzhzOFetu5iJLTJhKRZB5rws876cYUU7K4Th4n6vL0BJQiUhkDVMXgnttGoGpqVhvp4QzqbYKS4PR63y0NjcoPa2M8XVznC3oK0GakfbYinIrphSaApoqEOg8KsmSRJNl4ySb78exSYhF1hICk3G+i8GMKyZ/z5EWkd6uM1q7+2yvz3zBGuK/E3wvLb93L6l04Tq4h2G2g8bslsMDV7x1hrZB/dgv2e9JjMjMS3mxLMv6XMG7ACSx55pjHhbYS8Qs+MWqueNGMpsIFnC9ZrdTcrRxpnMeQ6t3Z4u9oa4wQxssRfLJRWIfU94XCk/mLi5AdPs/xde4x5PIZY17jdjjnfJ0Ul1YmocYKfqc/XiPPEC3GSsKWTCW49N6k+XJnXu3ko3/JaXNYRdYWnV/aom4hKZFmX+XN8D991xXew1NkDYn6x9r7cuY0KPM8Tx/awcYbwmqtfg2rDP/3C/8Ijqw9lcplRzZ0KmrJGZhJSNflp2hyv2WlcU044fZCuQ1cNGGSlp6BdR0SJG7X1Cd7uUMYoSGYvOBdQF4mbSlxtSOtKiorsgvJQsEVCLahEiiOFIS/XEvWx2r53XXO1qmhHKG4s8Acd0vcQIa5GZN1aBDdrjNBWnCqVlljTsK08AsVNgvQ6jO+rieehPttw4d+tMf8dc3Ru7hKuE/Z+3zIr/26FmO/0EsDtCcQTtT1HQSAY9F76hqJsKwfFkJ/is09tVPxygTywZaswSS5tgsnrUikzHyUYFmMi+zdhopIvyLyFEWPvTsShaxvquqEpaLHbGKbuGluV+hmH7C2JtyvVp8ec/dA5dn/nEo2rqIbC3KDProMLNL2GYWdElWqabiL2G3zfUY1rq3TK3HZlKoDrtXiNDDH39ho4L4bRcIITqz56nS6qiS4ld8ur+Uu7vpcb527Ch4wO1ewRfJFFe75eXDaJAwARPJ7XX/NG9s4e4O998se4f/1+8IqO8tyiMVs91wugdnlIbmk0Gzy1NovGMxCac43xIshvbIxyH5sGP2NPcWptGUWgSsY67WclrfVI2rQ7pJ9xFFeUSN+qjDQ01XD1JoOnQ/KdNW3xPwqhc10g3FzgZjFoeAKtbY4Q1yLN6dog1rsCDCPxbDRE6IwJB6VTNf7Kgs7NXdxioPpKTf1oRbMeWf1/V5lzid41PcJtjr3FPlb/23mGZysTS9olxLPWNtkD50FycEhHJ0M/EcfEANsJzYWG6tGxJZHsuGZWjrkKcbb9sBKe3NIYelQ6WAtUqn0M5As2b2Y6+c6fa/uyW1JuFGx+YYPxqDIG6lJJ2G16o1p7axNuEuKJmmql4vxHztG7Z5ZmZZNxMWbjvk0WFxdYvGWR9YVNxqEiuD5DGRGLNZoCEzQqjRQoXZd9Xm3O5Ry5ynA5cdjz5ItANxWEFNije3jX/Lu4e+kV9EKXSZLY0rW8aJfLnxSXTeLIiTobjXleuPcOvvvK7+afPfhz1GWi7tQTnkaKiheHFoo0yWRJ25465X/QO8QIBMQLFeXhLq7n8KXJ60Vt8qbG9Es1qc0E1AZyWqtBz+cw/c8R4JTyYGnaHTOetNEQFm2NGc9XZtvQksoyAct5xd8UKG8PtF6wOsoMTJc3RqUlkeZsRI43yJJHNwR3KuGvCqSOkpJQHaspryzo3FBSHigYHwyMPjkirkc2PzoAD52rupQ3wuLBBfidVUYna6sc5p1xQBTI61SQLZ1RIW8/sthPK6dHTn4uJ5jMFXIOmxVNmKdYleGxyqkjxgvJnA8JVuH4mWxd2TUofFqL1McT/qxw7osXqNYs2enRmjAL/bfuQeaUVHgIkeLKQHlrl9G9Y1vJfnnTRHh6iaqouPDYOt3HznDlrVewuGuB9aPrDNY3qZvGztNRO0+eYUiGiUsWJrK3kRHqChfw6mEMlW+QRri2uoZvuerbKL3jySAO2XofXwJx2SSONlRAsn7BPQfv5he/1KeqV+l2SrSnxI5aGR4TLuQpfDLgFeSGpdWu7DviWVuv+j2Zs1EKhQS0bogdjI1bGTdFO2xN+JOJ1egQ099sFD8bkAWP76pVApUQdheGIxg5VExAB0+GkStyhae4tbDyPyb0CaV6tKK4uYNbZEJ3l2BKaBrs9/BLQri6MKyB2EwircP44TFpHXq3dgiLHr/kGfzBJvFMYvSZsSEbj/TxPWX21Qs0H1wh1oYhaYl7LUBMU7vV0bwdkUwebDkgmMzANmKhZP2Tra0KWxqpwVoWw6tgLNMyt0s9h1v0+FlDc/rCMyezXPjyGsOjGwzjpnGNZvNGJhomRoY2R3HRBuF+JlHeUBJPNzTHI/F0JLykRFWsYtifGK+NeOjzjxh+xCVDBjtwtSKhsMThMsUgtyHOZ7ElEYJ4QvTmQUwNpdANHYbnxrx0z8sog0PFAekSaEq+flx2iQPaLZZwePFqbtp1Ex87+TGqTROadcFeaHWK7zqYyUzYCpDcL+c7pvOeelAR9heE+WC2BcHhGqEZJ1zfJOxV80YhtzvtGXSQkJGzSiIpfjHg+3mV2vdoNxv5dGyQF9canBgBz4S8oq1r83q3PtWQHjV4unMO3edt07PsSD2QcSaGlaal6XcFlGTFgUuQjaBGj46J52pmXzZL79YufiGw+ZENmsdrRrsdlCM6+0rcbmHungUu/H+rqCT8bsniP1kEqQVYTXxPodV6VZh4xqZMSZ/owLbbk5xILPHJZKVJgSWPnthQtSsmw9jPX6+F5njk/LELrJ8YWCUgxpyVyR7XBsVxGCkIJNGJ1GHYEyhv6JCqMboaSacSxQ2FyQmqRzueNGcIXkl51tLS8NczYKyfldB73nA7M2TNDKGWZCpmuSINRUA3Envibl669FJaTdGLLCP6jOIyTBxbvWIv9HjFvnv4ozN/ZHcOhUQ2o47YHarjcD0lxgYrvZmUzDpMuAXz+3Qznm6nS1l4ZGx0aRds+BWbhoiBlCS3OiKYn+taQhv7op/PRLbC1oahH4xQ5jyhsXI+uWSrY41Gq1+yCyLVdoHGYNuK6pEaeaJGx0rvzXO4XjR5v8Z+f9f32TTIMCBOHDqOMBK8QvVQzfqZVeZeN0/3SCB85wLrv7tGPNZQLxhZq1gEd61n5lSfjYc2CbsDzntG9w7NKa7OQ2WnIC77u+bnIKNwt2w3sbXxpNrYRiJz7fNuuAg62NqzY3gImTNsiNko1sSTiepEvhHMKura7UxOQDIZdRO1odBgbazLnjeFEA56ylVPlZT6WE3nyhLZ7W3uNEq4ZDcD17U9sWSsjJa2SpdceUmQLVRoxve0FZdzghSOsilYP7nBW3a/kgOzV0zeqTu9OfmTwu30AS52SC7+VECc4859d9LzXftz4XDldhk8+17XNU0F540mbkzXQEwJvxzwuwLd2Q4zRY8ZP0NJie96fLA+1syPbTioUbPVpG1l0pmGVCt+zhsCUzFuSKMmbuuwKqiTJ/Q9h8w6yitKipv7hIWwtQIsMUTifrNf0J5Dk1B/dmRVT23rSnFicAjF+qYM7257cgXwMD6dWP3NVeIpodjrWHzDAsF74klbHzfDBnWJzgs6+FmPmxf8YW+GVTVosqxgIE7bqKiavYNGE3NOtWmcbNHe89Bw0tZkDY6AbWuK3K70BTfjkXnDTKTVRPV4zfjBhvHRcV73KnSdnS2L6rgZA+q1ehla5OdE7EKV4HDBEeYd4WBBWDbJgOEDY1wyrklYCIQ9ZhspXcXPg18Q/JIn7C8o9hX4eY/vOkMNZw0OcflmEhzOe1xwdF2P5mxN71yftxz4dqs+2rZ4MhC99OKySxwWeUilypGFIyx3ljO4KPuLeIHgJyxF6Tn8XDERrvWFtxnIrKfY5SnnSzqdEu89SROaIr7n6LoSN84XTLK1r6l5myo6KHGgyEgp9hQT0yfTx0i5FLaE41pYcgBXCMXVHdPG6IgRpUr7e9c1EyW3LyMUZ6A5XZNWFYYJbYyH06w3pFPJWjBt5QvV5iQZ5kxSqtMN53/1LPXjibA3sOdb9iCbQn26Nkp9Auahd51tACht+Js2zIeXFvW5vezOyO80bhXZdCtxtN8g+XnLFYhm3AalmKZF3xu3RaF+oqF6aMT44RHNiTq3Yg4363EzbgtP0TebB9/15hUzG6xySW6SMMXZ5sfNBMLegnAw4PcENCbGD1T4YBYWrusJc4GwWODnAn7Ob/378+ZDIzOCdD3toki8x4eADx5fCqEoCCNhGIfcNHM9N+y5Pr/eeRA6rTguzVCBxc4urp271mC/7SYwOLvoijwdTzZQxRlVPmk05es9gWKXEZK8OCSZXP5Gb0QnlIxHNZvV0JTHHGYtUCWbabRQdTFik5uVDD6z3j9uJOJaIjXYMDbfrSQL/6bMqHVZgq7V+jR6va1IpQfM2goznjGynMsq5BqV0YNDdNNNpAK1xs42uYitfatXGi78m/PUTzRwjbL71t0murySSEMDyoUrA2kjkU4m4nnzjdVWtUut4oD87yrmq7KRJqQ1kTz0zYlbt6NGA0aJL8Sqsr5QzHu8L6iPRqovj2hONWjKldmcs+pnzlzo/Gw2zp5xJrk4k/1iO5nwNkr2vLQXqgPxjmJXoLiisJZw3lOvRqoHhwgOn9Gw0mlRsbmtyurlYT4QdhX4WWctUn5aNc9rOtKhN+qyPhoQUuDbDr+GXrd/yc40vjYuwxnHVpj7ecFVc1ehZ+02KH6rVLaNirmJqRP8jAn+as8o3b7vCd1AEI9znrgeOXvmvM0aXCS6ZA5hqb3F5moi40IK5+nt7uKuDTRFlS0As9taDXEQiVU0eDJM1rtSmNtbPB8JewNa250yxURajfiuKZ7LvMNdUFKRSCs1LHl8vyA5xReO+nyFfHlMuCEQqwyP1/b2aIPEJEY9b45XXPiNNXb9wDzdO3os1UucO7FKNVshhztI6dCBMvzqeJK88g57YiQ9sTlE0SoSN2OmCmVCoQMlbaFB243KhMTnKLuBmf2zaFTWP7/G+LTNMiQAknB9sbt9yxT2mYQ4aX8ygjdXVIgwHtT0ej2S2gyJfFbxUOzroDU0SRAfqc4m6I7p3dBDfF4hZ7o/rn088z3xnWA6tzVQ5eQvwhwzpNMN5zYv4OccN/du4U3XvQmPDaufC7njsk4cijFnb1y8CaeO5Oy2MKmqW4So2N0cyP4sAXFqxkfO1L+bC5GVh1YYYXyG0HdGd5dc1BnPzZKRQFkEFm9aIs4nqjgyXQjF1L+zl0kaJuLZBtnrQTMuoAU5FUYwG55poEqEa7qkExkgVsqEPKZjRRqHNlB/qcLd4Ql9g9XjPPWxEW5/zzRZUy4HBLvT5xIs+0IzfnDE4JMF+irHzJ1dFu9NrJ7cwHVril3GCnZnFf+iLq5QUxVbj2YEhcu8EbXntVaklkkF0mI6Jo/dIkazP0mYFcp9JaEoWL9/g/GpkckZZJSpZknHsLswfkjXbRlE5SHlpB3x7XBUTHFeEtXaiGKuwHnT+IRcDRbQvaLDuIYmr4TVJapzNcVuIyCqA8FPMBoiZgrtM1YlRiUeb4iLiX4IbK5tMBiNOLhxkFfsezlvu/IvsX9m70Rk6rkQl3XisBD2Fftw4knY5mTrrzILEXsjkyBV0XQ5y4JQFChKnRriiRHD0chg1u1gT23I6TIIanLn9Y7yYI9hd2Qap8KWyXHElM8FJDhzTy+EMO/sblYkXIkNPhtFUkQHUN03Iq1tSbvaHdjh5iCOlHTO7Bua0w1+f2lv8q5ZC4w/XVHcUhqCFjJEXLasBchbAlGGnxhR3tZhtHdE/4V9xv9lRHUq4jqRcIXHH/D4Ofv900CpT0eqL1boWtrmJC+2Xl6186oD53Ml1jJknWS2MnQWSvqHZxheGLJxfJ0UQOYElwfJ0qhdrH1wC0YuM06ITMBmrXu8Y5s4Tlt5ILDiGXx8wOxdC+hiTSJNXi9KsZZlyU+Sjm4mmgs1YdYTZkoTiHZ+glMpfEHfdyhdCXPK2UOr1M2YjcGAJOAbx1uu/7P80M3vovAexU38ep4LcVknDskX8975vVw9e4Svjr9KlLg1zDMMjl047eZSTBV8PK6pUkPoBbq+xNc2wNQGcydzBkhoRwWTPWwQiuCN+EUipVyeJ93q/aO24wV0mBg/NEKv6uDnMzGq9EgHXJPQ5KAPEpNBp1F0M+Lnva1oFzxaRXToDKexlqCxntv1IF1Q6sdqtEqUt5R2hw8OQjL90Lx+1mQs21hFxo+M8LtnqLsNC7ctcOa+s8T1Br/P1tKuY7D8NEjmibvkacYmuKxZS0MbJW0awEm6OsnXkmcAFIYCLfd28HMF6yfXDP4/LwQnsJCLlUa2bCJbNGmBtQ7eZhWtwHRW8TCWsDffWO887rRj9XMXqE401FXD/N0LyBKkOqI+J+xS8KXPlSjITMCVBU4EjbbCL8TjY8APHLLoSVHZHA8JySOFoupwtRJqz7d07+F7D/8FCh+MxkDe9D1Hqo7LOnG0LclNyzfxL7/1l/jNR3+TX3v033Jez9sKUIXoMvbBbVNi8i6TufIqsU6c21i1Vrdsk46beLOoM9q0jaIV37fbnwG5UlaxzlVHtPmGZuk6LRy6oVRfHuIPlIR93nxUC0F7phITa0UqMYRrVNIFJex15tqekmmgbiRilWAE8YLiltTUu7ABX1xVxp8aEa4tkFmrCGRkUnhmBWEgLTdnAkRxvaGeF5olCPOBuGGbJBFjBVM62/TOeGQ+IhcE3ci31AS6afwguuDmPel8stauKzbY3BNws54o2RiqY9R5471sbb/amUIzajImJPdVed9sJlKyjT0LiidUDj90VBcqhudHxJ4iy47h+THpvvPMXNun3FPiY0BnIrVv0469li4EfOEJ4lEfSONIVTVUvzPg9sELeP0PvJY/iB/jUXmEgRsS60S5WbCnu8yblt/EXzr8veztL9s2aZtuzHMhacBlnjgmeI4gXLl4BT90x7u4a/kufunhX+LjZz/OoNqwFauY/aHkDYdmAI8PDo8jnVW0ZyIsk7K+aO98bAn/ZKwAndybCBmabetajdk2od3fC7ikxKDoSKgfGRNPevwuZwrg7SS/46BIsKEwtpWvDtSqmrEgqhRXd0jNCF1LNMfGlFf1TUwHDOhWGlN2/MkKv89TXBOMMBchZQ4MHmTJWoDmQrPFWu0J8ZEIB0BmFZ+9ZcSbPqibc8iMI41ilifISdub+TZJbS06B36vx+/2xjoushyiezK/pWXPurHQrDRETUYq68jEIHuCVHW57UMQDci6UB0ds3F2DMvesDFz3sBlvQZdh3oQGVwYUR1vSGcb9r1kP7p/SNRESgnJGho2PDa5Qi8FB768h2+/6s/y3a/7bvYv7+cvjN/GFzY+z+fP3sdYK6654lru2PsCDsxcScjtqxWiz41ksT0u68QxqSCSvXBBPHcdfCm3Lt/CvSc/zX999A/4yPHf52hx1AhtA6iaMQ4jVQXnOZKu5tQfnoTFNfxtAdRg2+IU1wsTT4w2USCWILSdwtpJJhwWVWsVbPOiplCeMMJdcqT1RHOyIW2oaYvO2To2bSR0A1pGXnOqprypa1uaqLig9G7rM/7sgJSEeKbBX1miGyPDf8wKsRGI0bRILyTCFR5Z8DivaJGgg60XC4eOjKQnLpJqpTlluqFhtzcxku1tQnY4M7VyaNGqzAp+ORDP1Ph9gbDH4RYFPy9IRra6Vqy4BamJ4GpHcyEyPj8yR/rCFOtbVKxE+xnfdZbEo6DrkfrEiOZCbYVHnqvIIY8vBAqH6wZSH2vpzkYGpyuIwpnPrbBPl0n7YMjAwFs5ITnAjwuOfOkQP3HX+7j5qlsmVcNcd46Xd+7h7t0v3xq4t3DyPBVuRyzPtbisE8dWeWj/a1+/2c4cr7zqW3nFFS/nu859D79//COsjM5yqH+I//3+/4OVdJLSFbx86eX82O0/zuiOAf/6t/41nxt9jkf3fJXNajWrhLHV4iCmQoXSDGq7k/pceSQmMw6ZMHC3vanynVO9oqXA0NaKOkpGeutgLvEeWoHmtObQlQaWHW4gxEYJnUTnRX3GDw+Jqwm/R9GhQ2Yi0vWQ4gQSrUNoHk24uQiLGfcwk3kZktuscUIHig4i0nE05xJlI1utl5BBa5Y4XNjCisS1aG1XR3HLHr/PEXY53Fywx+q6SaIA84R1w0C9VjFuzAlbZv3Wc7SNPJiahDihGWDI1EEkrUbSeTWf4CxGmy406IyDA87kHFuV+Jhh416I5xKjc0Me/+Qx+of6zO7vUPQ8ZK0NKYVXjV/N21/8dm646qav6TRyVfHHqGrtn+U5mTTgMk8cTx02X/BFwc17b+bG5ZtRTQyaAR8+/p+oBiPecuDN/PXb380Vs/tht/BTf/OnGNRD/t1XPsA/f+Cfc258joT1w5pkst6FrEDV5HFd+4aHbPykWzwOwWYtuVIxPgWTNsig2vbOc74drkkesiaqY0pnvkRLE46JGya86fYUpNVo7UWZzMKhUdM1nVheYsCrlhZekrVBs/dtTnZxzVqtcDBQfWFM2ogGfMmzCNQUyq3ycFBhRk0dR7HsaTYTxd5gLcq8gbN834SdTa/CETY9w8dH1AwNKds1aLg9mZhsQZ1X52rPjet4A6fVRkbTIdvMxbdmCs1KbbiPrJtBMIU2IWuRziVbK682DI4NqLWhODAmREfhuryufjU/cvOPsNRZYifFgy92TBPH14384uf2wmw9hBmZ4a/d9FdJJO4++Ar6hWniqYLznlk/x/fd/pfZP7Ofn/vSz/PQxkO5dWjnKXmYl5RYJxinrEWRHw4mQ1LJfxbNLvGZSEVMhqLMvJZJ2DebF2zWfdBKqR+NFFcXBizqC3HdHledMl4fGT09SL7gZcuDpaWw97A7a8+4NBT58RyGgTgfoVQbZnYNlKa1rVElJxfVvJ3xTOY8fndet3YMHi594+D42UAobQhaqMdd8Kyf3GB4ekB5XdcGohmNKmrDZTJpTjTPQIpcgoBR/StyGzNBb1jyUNvKxDM1fq5jFH0xPlLKA2hXOrQLri/EdYhnGspdDh86FP9B+B/e+ldY6ixtmZVfHnnj6SUOEXkMWMdGd42qvkREloD/GzgCPAb8BVU9n7//vcA78ve/R1V/91k/+Tc7vqb5tBIeXn3ktQATc+ut77I7felK3njNmzm8cBU/d9/P87Hj/4XGCY0mAzWlZPJ5DaRGt0SCHJM74aT7bQerMFFhb/kqUriJiHJLhpL8M6YjIkgt6CBRPTamPFSQSltxqjMB5VQIpIQUDj0VEVETMk5YQulgF3Q/f56l+iQnAHWeNKxMIbzvCIdL4lpjqmCtHkfMFPug2R1ekb4QlgvSZoNbyB6rez1FvySUgVI6dJqS0cMjztXnCcue/nLPYOgNpBpojCiosa3e7Hwk0FHEdWwdrnWyliqrsyu2ps10GBBHcyHhVxLhQLBBqnO0lrGSGbtuV0GYUxw2e9l7epkfvP2vc9t1t9OSIS+brMEz46q8WlVfqKovyX/+UeAjqno98JH8Z0TkFuBtwK3AG4F/IdLCb57b0Rr+2t3q60B18t85EW5dvpWfvesf8QM3/iBLxW46RZnl8m2Vq0nzsNCGlybvn5mzkwfTLTRh26609PJ2w5AV1SeAp1IM5h6wO2gBDJT6sQY2c4VTOOiY6E1YLibeuJqRkQS1zU8Xw4h0bKviOpmdW7ZJTY1ElqX8wh5vM4+Rwaw12qxBOmSOhp3Lz3lkDtJIYQb8rCWNTrfLQjWPe0g4+/nznHriNJFoVpoLnmKmNM2TmWC8nBb84B0ueuKZSHO8NlV4u8VleQSghgnztn29Wri4Ks3xmrSSIfdY5WHbnMxq7Xr8TMDPCMVClytecIC3vPHbCb7gcoxvpFX5TuBV+fNfwcyo/07++q+r6hh4VEQeAl4K/NE38FiXVMi2/2+P7R2uqLB7dh/veeF7uH3pNv63+/45D9YP0PSEWESbYzR5/rFNIk4yxLutNlp9ihZZKiLWLtTtA7W9lLm7ad4SSSAnoQwsGyeax9T8TfcYqY6ZAGtQr2THOdp2KpnYbs9NAFmm0O5wXbaEdZKga4rstsTl5uwiTMOEm3PWJtgtHMmmSRIEv+ysalBLer7vaR6vKQmcOn6azcEmxXKZHdzaAan9Tr4IuFJxZ2Hw4CY641CfYAypMad6rQ2Bm6pkQtTjXJo52XqdJoQ2G/amCNWxMc538cvBqoykmPAphttRCLMeP+d4ghOcGp3kmnL+chlrPCmebsWhwIdF5F4ReWf+2j5VPQGQP+7NXz8IHN32s8fy154UIvJOEfmUiHxqZWXlT3f6ix3t9S3ydd8ssu0TFUU0UXYKXn/1m3jdlW+kGjWgSig9vhdwvTwEDLnM9tjF0iIdJdfTOYFIK0ST6eWUJuKj3oaaJjAkE3cwabU5s6cJoqQzieqRmnhGSQ83jL84QPOsRVsbgiDWCnWNYTsR/s3sYNf3BmLbEJrzdR7aGj/E9Rxpw/AoKebhr05KNeiC3+OIZ5oJ+7UcdxjdP+L0Q+fYrAeTuQsxz0lqhUaJVSLGiCalOZcYfHHE8AubxLPJZh2BvJERA9GNQNdBN9LW6jsPmNt2a/J6Bqvsqgs1ab3BOW9m0aXpffh+oLOnQ9hdIoUyKkY8PjjKzhox7lw83YrjHlU9LiJ7gd8Tkfv/hO/9evn3ryQvxgAAGy5JREFUjz27qvp+4P0Ad9555/Pu2RfNRLIE4iP7Z/bi1NGM6i04ejDX8piFbYxfJrm8MIh4q8mJZqq5qrUkBeQMZVWKrW0mZY+0M5pWUWubcbPWUJ+s8mYkVzB5IzNhkHbExH/7Nr+gi6mhdUwDxHvH+PTYqomEaXgU5oiWBjqxyXSlkJzNdqgUt+Ch49AVxe1zeFXGR8fUqcFn93jEWaszMHRpGkRSm9ii/Y5p3FiSHTnqR8eEwwXFkRK/4HAiVMcq4lnDo4g4ZJvmh2RsSIvsbWdGrmeD2uSUVDX4fjAOioips3UtoTsREpFB2rQbxGVYcjytxKGqx/PH0yLy77HW45SIHFDVEyJyADidv/0YcGjbj18JHH8Wz/zcCMHuRnkjc/XC1XRClyalrIBl60/nhaIsoYSY4qTnTq25kOS1puR+vU2xOUFMbqSaV7PKZC0jbF0g6ow2v7WuwQaX1gs86euSvVv9rAGyJvT0Uqzi8ALqqE6MbX1ZYwkyKMVSSXV2bDObbRultAHNJvSu8qbyvpmsDcAx3hhN/FMp7MJP5yNaKXFT8UOlKVJG4NpFWp+urEqKijRC83gNEcoXz6JDpTnakFZjRpFayyFtNZclCSfs2Wxz0QrwhI63ta8mgithwWdRZ8nsV1tRV27EZdmn8DRaFRGZEZG59nPg9cAXgA8C35+/7fuB386ffxB4m4h0RORq4HrgE8/2wZ9bIeyf2cdcMd+uQSbVhUaohzUME0G8Eam86W/QmNqXCcZk/EJrL9gR0+no+i27xwJcHohqCxHPyErJbUw7YJWCSYs0aW1aY6MiK4INwWnAuyI7jtmMxSdPfHBMHJuUn47y7+NNaVxESOtK3DC/mPqJmubRMVKBX3LEM5EYsbWqCCnp1izFgfPOlLycWUQ2q5G0EUmDSKwSek6t2nGK6bAq1ELzRGT42QGbH1ujORdz4sQSRKvH0VYY7XaqNCCXnzWJQfHmGVwQ6Oyewe8rCN2thOGcUeYJwqaOvk4tfXnE06k49gH/Pg+TAvCrqvohEfkk8AEReQfwOPBWAFX9ooh8APgSNh57t5qf4mUborA8s4fr5q7h5OAkoi3t1toEVaWKDW5gMHYXnFUc0ZTSVbEKRTEt0nyX1bHivUfraDqlKa9lRbLiGDZQpaWotxNYJvwQYYJLswrBkf1MHPF8JGalMel7U9DyicFKpNlsrBPyNgQ1ZTGH6xjmJG0mdBDBe+LxaGzSOYefLxh/eZO2I9OohjEp83zGQdpMVKdriqIwNbRzTU50gi8d4/sHpJh/D6e4lD1wVKmOVZlgqPa11gFe2GpPXNueYDOmniFVUfPT6e3vIvMeXLLNSh6QqqSM1XDMpTluK2+x5KWXX9Xx300cqvoIcMfX+fpZ4DVP8TM/Dfz0N3y650moQKeY4e6lu/jDJ/5wonkxcWEXa0tUlTpGaMzl3Xe83d28kLDSX6N9lGgQdIDknAkDtx1H29a43Hu0YkRggjftmnQCh2dL6zQnHVq4dz5jGptMoI7ULCO7eSWsJjiUxgk/k8VsHEjfmcZpihQ39GiO1ebyXjqzphQFQjbldkaH94IkT/34yCqxE7UlwV5WCu86mqNDxisVE4Fhn5+HljjnAJ/QILZEmQxC1aQHs3apC2Kyf10h9ATfcxR7OpR7S0OQCojkhJGxZE6t0vAIr5PXccvi7U8G4V1GMUWOXoSwMYLjnqteyfsf+iVW6/O2ZGjVqWCLVQuZ56IkTVn0V82kOOQLvcVdJKBKuMIGd6mOE8Wy9nFtOyOQkjm/iaDiDe6eB6ItDLMVIGq3Ou3WI3sDQZ2/Pusmnb0mYGzaG+yySsgEnUEPF6S1hGpD2Ofp7esQUzvA3docSQfbXgyF8VeHxHHCz5BxLBAvJIafHmVUbbasKHWSOLcGwrKVKJytZF3I1Ug7zyiy8HNXcH1vuJD5YJ/vCnmNIxMwXft822rc46rE7byIt1/xVyjK0mYsl1/BMU0cFyXUqoAbd9/Ay5bv4sMnfje3BfmN72zeIeKy6lRWn8pSd1oJcb3JF3i+4FrEZGNckHJPQSwSaZwFi11e7UY1PYpkqxaNmJN8Fh0SBVLmn+QkIYEJNoR8V59cRF2ZVBqSy3+czTPA/GDcbB4uAnSEFICOUhwI1EdHSMfMoKXwBusOjrSWGJ+t7WmZMa0RbYBBtLYrt1xtlaQZoKXYrMSeqDy7aGdBWQ5AXLZlzHB01zXsiAQzyNImEvqFmYWLklKyWUaLr8mJTqKypHv4od1/jeWZfa0r1mUZ08RxUcIuwH7o8Y5b386nznycs6PzTwKSiXOTdqFFfAkY+hMzTEpVi0fIg75s56g1jM/VFLsL/EwiDVuIdVtV2GanvXtO7soJW6fmhCTSwsMlX1S5+siVSYpNrkbIF00WInbWrugQYtVkEhp25/aKdAMyAgqhGUa0BKkxpXDENi7jiMwKfjaLRLeK6x1HJCHDdhazxRqWIBPYPTEnwSwELcESkJSm4TjBwBTmdULGMgsmEOQ7pgnotK36rOJz3iM4fBM56A7xnoX3cMeuF22tYC/DagOeGeR8Gt9ASBJwjjv3vZi3Xve9BlVWvwWMUiZVxpYit131UpBBUbkcl7xSzHdQKWzVWp0YkdY1e9jmB9a23M53R79VUWieAeA0O8ALdF12k2MCERcnaBOt0uhs/dfaA1DYWauViqjR5g5qw1tVkCYhrW/LGFzX/k1JiXhBSRINuNVz+I7gO9nKYNbhFrzpiJaS+TK5cii34PttJaGZMdzOHSTT7AUmiViyGrlrE6IIYa7IUgGa5xl5dhOzR47A1e4G/ufl9/Gty68ybdHLPKYVx8WIdvCIEoqCH7jtHTy8+iAfOfVRA3jlbYbkAWmbHOxznwd7imsgZa2LyfyirRBU0Upozta4oafYFWBO0A1DVYqTCW+j1dOQnDRa1KdZQkpec+aqJBgGgwxJbyuQNtmZpKDDFc4u2p6zmYsX4vkaVWguJMrlwuQEsq0mPYPWN+dqE1/O/jWaGaZS5MRQKATF9RNJxZ4rDzLjrEqq8+/Sbk5yJdSuYidzm9azJZgEwWRTOxvwZVvh2SDZOhNBGqtveqHLu+bfye0LtzHB/1+Og41tMU0cFyuEyRtz9+wy733J32HlD87yufXPmgxexjN8bek74VS0LmYJtMotSgsOay+QYNdyGiWq0xVhPiALHoYYlyMCVU48ufLQYJsOKW2FSUvzd6ZpQRWhp0jP2UYnS/Olph3mGrjKz9iGwpXZ6Agjr40eGkEDfs4QpRTgZh1xpEjWv0iNrZ61vcC92Ooz+9C4eYG+N1e8hDFtS0uE6siSjmLKX7F9Puw5n3hd6rZEkrcszjnKXcaJIUquRlwGs5lDnmQrh7mwCLgtRO5lHtPEcbEjS8dds3Q9P/uKn+F9n/hJPn7+E0SJW2/HbXe0rXUphsNQbPXatKQ0MuVeTGGssFpdUeq1BjcW/FJAu4obJlMKV6DMxLSSSdkvLU8lSDYaMk2ecs7usilpFtAxlXL7XVpglW1hfJmHkgq66CkPQ9JIGoEOmgz0cugwTYBoVv1YpTNh/W5bUwtiWiBz+ZLNZllaq7VTdU4ijc17tjROJG9IcmvUmiVhQ9ViqWMCRZpbK4V0OqJrke71fVxX2K17eGvne7hx5sZpvtgW08Sxg3H97hv42Xt+ln/4qX/EfzzxH+3OJ2p0b7ZaF2gTiF0ovuuJA52wZVvtVFo39OAM7h5Bm0RzvjY7wsVAmklQM9EANS9aZ61KrnxEPLoWqQeVXaTBhouhY0baImIrTieklIgGGkFVzVs1t2aCIyw76lGNVjDebIyYlxIyL5mYp9liky31+Bbhmtsm8YLPCNlmozEOTNf4M1opVKBjNbe0bOfgSme/H9sGvJCVyRyh6wiz2yakAroa7ZzHKsqZkhdd/RLeve9dvGDxRYaneRL/+fKOaeK42JFblrwc4cDCIf7Oi36EY4Mn+MLG50j5DjvxWoWtxNCCFAvB90ygtyXHtchIpL1QbPUqSSAm4vma1PPWfrhk5LWuQaxtc5CTzDAS1yriOFPyg0zmB24gE39UnCWf1l/XJSGheDyl87hUEKuGzfEgD1VNu0MH0dqCGW8Vj/OmQ+IwxKdghDRn6FU343El4CGJUpQFOjRfWoJMkodWpgNCxPRGvGybb8gkOVhihXKhY9kqyeQ1Sd5BkVhcWOTPL76Vtx/6fpZnDuCyOVP7+k1jmjh2PITEwaXDvO/F7+Pn7/95PnnuEwwZmeNY+2Zt8QTtTS/ZQsY7mxWoqsHQ86aFTMKyAYFAsFJEks0/BCX6hJOCIgSISj2uqap6iwbvjNlKaG/+Gb+RmbbGjTF4PArOe4ILePXIpmdjZZ1xtzL7Rg1QKyKmPGYbZ6ug/ExAvQHXJDhwud0pJXvUbM19HNYSuXlvS6JKSbXieqB9B423GYcXtEmWoPLZnXN5WSKEXjC/GLGqyEZFts05XB3hr97zV3njkddTlKWl+OQzQ/nyxW18bUwTx05GrhQccPuB2/mFxV/gtx/5Tf7VI/8XR6ujeXGhJJ/sjp7RnUpCgoHAXM9BlYhgymBFbnEiiHUQk4qh9RkBgQTV2piaca4ibKiZagw01hXw2fw5DxVNNlAyUSzPDMxRiFjVRCdU44o4iCQUl5OYAqFTUHdqm8OoEcnabZPv+WwD6TMBzZKXJp08/iREtn6XruDLPMdQ0G7+FgdaBbNMSLrFkE3gUEKmyqMO55WudDgcruIN82/mjTe/gf0z++3vaYWN203LxXhTPDdimjh2KLbehELblczOzPIXb/k+7tz7En75gX/F75/5KKtxDR+F5BzSCvC2/bYkqyx2BfSMvcFdxxk6tLVADG4yVIUtrIhdF5pNme3KjC3tvuMmaNaJn0mwmQg+txDePUnrVDMuRNVaB991kw4qpYQUwZCiKZrPiwqu8KgmUm0VUyoSbjZMKisHNu9osSgw0SB+UsvgoewF+huz9Is+Z/1Z6k6Fq8yfxta8Do+pmRcz5um6zBJ3lHfymoXX8uKFF7LQX8resluzjBZ6Ps0ZT45p4rgEwsRggGTo0VuWb+WnFt/HZ05/Nx86+iE+cf7jPDE6ziiNSaRtptbGOfGqhAOd3GKYZF47I/GFN+p569uStwx4Z050hSdpoqkbXBGyEha00Pa22nBZ0xQ1IRtRJi2UarJPReCcEjY9Oq+tegACNFWDNGpVUSfPZTThS0OGJs1JrnS2Fm2RreRqJ6+ygYnWqJBQcRQdxw1zt/Ceq9/FTXtv5dOnP8lHV36fzww+y0qzgo4b/ky6i5ddeTfnyhVm+7Pc1r+NmxZuYW9vDyEYWs7El/Rycjn4U4foJdC33Xnnnfqxj31sp49xyYUCMUbODlZ4ePUhvnL2fs5WK4wZ4yVQUpIvH1qPlrEO+ejKf+bo8KgN/jJ+rCW/tfiGlgcjUXCNIxWJ6BPa0kxbohtkHYotNKvJd7br4jQRVNZTifXfWqe8tkv4Fj9JZC2grUfJWBvqYY0OFcaJcq5DFStrS0QIcw5VN8lbrfH3JFqsiwLeURYFL126ix+94Ue4aenWvApWaq04uXqK+1fv5/TaGV571avZu7A/A+C2soJM+4+njH6/f+82cfInxbTiuMTD+8DemX3sm93Hyw++3LYuSbMa1h//fk3Ktx57FT/5+ffx+OhRo9/jICZSisaQzcI2qYE4aNAIoQiU3QLtKFHi1pYmh7R4ChVIaQKgNPSqII/D+m9sMFqp6RzqIThU40Tjw0YKQqGBVEaSmhN86BXEQaS+EGGoxMKYrxNwV7bnlFy9tGeSwtEv+rxx+fX88E1/k4MzB3O1YEms1A6Hdx3m8OKVWTXMI1qj3k8g6ZfCTfO5GlOuyiUckpmy4qzxUBFcVus2hzVpt4xZXVBwCPcc+hbed/tPcrg8RC1K7SqakAwchhBHiXozEjejXYgFNE3NcHNItVkhCTwOr94+4vHO8BtOBN/qVKA4F7i5uhH9D47xyTGuVS1rcRgR8z+pE+PxmNJ1mO3N0F3s0dnTQTtKaArqB8ZoXxCf0MbWpIK1bs7njwLOCWWn5NDsFbznmh/ix2/7Sa6YvSK3Zs7QtLlSycgXBEVSBPHZ5HkbL2gaf6qYVhyXcjyppN76ZPtcddKMP+nvlZcdfhk/yT/kx+/7MR4bPQ7RkVJtG40G85zN9gPt0JNghLVIsq1HOyCV3Oq0kxOxdkAEruvcwP961y/wqeLTHDtxDAEuLFzgN9wH2GgGeYOS7+wusTFax3uHBlspIw06B+WBgF6IyKLNWcT7rV8rtxdSBLqdDi+Zv5N3XvmDvOTAXZbQJhz/J2Mt2lXsZHW1/e+mSeMbimnieD6GCoLn7kMv433yD/j7n/0JHkuPZNEZMXxGMk0L3/cTOULTAnGTfCStiLHoRHhdsiF28B12uSX+2qG3c9XCEa564xHa9cPaYJUHP/kQf7Tyh0RNGRsCLg8smjwydSqkQUKA8rY+up4yb6eFb1gS9GXAdwK7/W6+Y+nN/OWr387y/F47Z6vNsSNP9OUb08TxfIy8pREVXnblPfxE/An+3ud/jCf0FGg0bELfIZWaZ2wpZrAsCs5aBRXwXsyBTW192pEeV/YO8pKFl3Dnwou5fvf1XD9/veEmWpyWwnxvnr910w/z4/ed4/61L9NgrYpqxkP4bB7lherLFcURb05ps46ggepkRVmX7D2wj25RsGt+F9fP3Mjr97+Wly6/jLLoToSOLZltcXumcXFiulV5vodCJPKfHvkIP3XfT3J07ZiZJdVmUSAe/KKZQ4nbNnRVUxLrFF2O9K/mFbtfzt177+bWpVtZ6u8mtK6eX3PB5oUpkuDB1Yf5mS/8E/5w5aOMxhGpM+zV5fVODaNPbdK9s4ffVbAQZrm2cx13lHdw5/yd3HzgJkK3y1xnlpmihw+B3DxN4yLEdKtyGYcCXoTXHnkNM0WPf/mZX+bzX7mPsxfOUqUGty9z8SegMME7x2JY4vbZW3jDFW/mFftfzt6ZfTjvtzYqyGQ78bUPKJiA8HW7ruVn7/xpfuuxD/KfT36UBy88yEazAQqFL1godrH35t2szK5w1cJVvOPaH+AFe19Iv9PDE2wGE21rM0GHwhRjcQnEtOJ4voduUV00KYNqk8fPPsYnH7+XDx39EJ8dfpaxjAllwUzZ49q563jV3lfxrfu/hWt2XUevmMmUvPb/2/7hrwOpnHxnK8GXAWKDasDJ9ROsjM4QJTLn59jTW2ahu8i5zXP0On1295cmw9jWQNcUvbaGnvbVaea4GPEnVRzTxHEZhbJ1ESZVVkerfOLEJzg+PM5SdzeHege5etc1LHQXaRUKn83b+5MSzyQZZD4IMl2RXmIxbVWmATz5zg3CYm8Xr7/m9falPNk0K4UthfVn7TLW7Z9mFGvWW5Wv/YZpXPIxTRyXVWSsg24lCuNn2N/q5EJ+sh7IsxGTx8j/25qP6FQe5zkY08RxOcW21mM7iGwbdmzbtz67l/KT/rUntSNbjzRNHs+deFqQcxFZFJHfEJH7ReTLInK3iCyJyO+JyIP5465t3/9eEXlIRL4iIm/45h1/GtOYxk7E0+Wq/ALwIVW9CfOR/TLwo8BHVPV64CP5z4jILcDbgFuBNwL/QqRd+k9jGtN4PsR/N3GIyDzwSuBfAqhqpaoXgO8EfiV/268A35U//07g11V1rKqPAg8BL312jz2NaUxjJ+PpVBzXAGeAXxaRz4jI/ykiM8A+VT0BkD/uzd9/EDi67eeP5a9NYxrTeJ7E00kcAbgT+EVVfRGwSW5LniK+3ozrj43oReSdIvIpEfnUysrK0zrsNKYxjUsjnk7iOAYcU9WP5z//BpZITonIAYD88fS27z+07eevBI5/7T+qqu9X1Zeo6kv27Nnzpz3/NKYxjR2I/27iUNWTwFERuTF/6TXAl4APAt+fv/b9wG/nzz8IvE1EOiJyNXA98Iln9dTTmMY0djSeLo7jfwL+rYiUwCPA27Gk8wEReQfwOPBWAFX9ooh8AEsuDfBuVY3P+smnMY1p7Fg8rcShqp8Fvh5m/TVP8f0/Dfz0n/5Y05jGNC7luCRIbiJyBhu6XipT0j1Mz/JUcSmdZ3qWp45n4zxXqery1/uLSyJxAIjIp56KiXexY3qWp45L6TzTszx1fLPPM1U5n8Y0pvGMY5o4pjGNaTzjuJQSx/t3+gDbYnqWp45L6TzTszx1fFPPc8nMOKYxjWk8d+JSqjimMY1pPEdixxOHiLwx63Y8JCJ/Egfm2Xq8XxKR0yLyhW1f2xFtERE5JCL/OWucfFFE/sYOn6crIp8Qkc/l8/zkTp4n//s+kyt/5xI4y2Mi8nkR+ayIfGonz7PjGjmqumP/AR54GGPglsDngFu+yY/5Soxr84VtX/tZ4Efz5z8K/Ez+/JZ8pg5wdT6rfxbPcgC4M38+BzyQH3OnziPAbP68AD4OvGynzpMf428Cvwr8zk6+VvkxHgP2fM3Xduq1+hXgB/LnJbB4Mc/yTbtAn+Yvfzfwu9v+/F7gvRfhcY98TeL4CnAgf34A+MrXOw/wu8Dd38Rz/TbwukvhPEAf+DRw106dByNIfgT4tm2JY8eem6dIHBf9PMA88Ch5RrkTZ9npVuVS0e7YcW0RETkCvAi7y+/YeXJr8FmM7fx7aqzonTrPPwN+BEjbvraTr5UCHxaRe0XknTt4nh3XyNnpxPG0tDt2MC7K+URkFvh/gB9W1bWdPI+qRlV9IXa3f6mI3LYT5xGRtwCnVfXep/sj36yzbIt7VPVO4E3Au0XklTt0nm+KRs4ziZ1OHE9Lu+MixDekLfKNhIgUWNL4t6r6mzt9njbU5CF/H9ON3Ynz3AN8h4g8Bvw68G0i8m926CwAqOrx/PE08O8xScydOM83RSPnmcROJ45PAteLyNWZsv82TM/jYseOaIuIiGBarl9W1Z+7BM6zLCKL+fMe8Frg/p04j6q+V1WvVNUj2Pvio6r6fTtxFgARmRGRufZz4PXAF3biPHopaOQ8m8OjP+Wg583YNuFh4O9ehMf7NeAEUGOZ+B3AbmwI92D+uLTt+/9uPttXgDc9y2d5BVYy3gd8Nv/35h08zwuAz+TzfAH48fz1HTnPtsd4FVvD0Z16bq7BNhOfA77Yvld38DwvBD6VX6vfAnZdzLNMkaPTmMY0nnHsdKsyjWlM4zkY08QxjWlM4xnHNHFMYxrTeMYxTRzTmMY0nnFME8c0pjGNZxzTxDGNaUzjGcc0cUxjGtN4xjFNHNOYxjSecfz/qxxe3WJQNJUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 红绿蓝 ---> 绿红蓝\n",
    "# 花式索引\n",
    "plt.imshow(img[:,:,[1,0,2]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "275.391px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
